Artificial Intelligence Nanodegree

Computer Vision Capstone

Project: Facial Keypoint Detection


Welcome to the final Computer Vision project in the Artificial Intelligence Nanodegree program!

In this project, you’ll combine your knowledge of computer vision techniques and deep learning to build and end-to-end facial keypoint recognition system! Facial keypoints include points around the eyes, nose, and mouth on any face and are used in many applications, from facial tracking to emotion recognition.

There are three main parts to this project:

Part 1 : Investigating OpenCV, pre-processing, and face detection

Part 2 : Training a Convolutional Neural Network (CNN) to detect facial keypoints

Part 3 : Putting parts 1 and 2 together to identify facial keypoints on any image!


*Here's what you need to know to complete the project:

  1. In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested.

    a. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!

  1. In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation.

    a. Each section where you will answer a question is preceded by a 'Question X' header.

    b. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.

The rubric contains optional suggestions for enhancing the project beyond the minimum requirements. If you decide to pursue the "(Optional)" sections, you should include the code in this IPython notebook.

Your project submission will be evaluated based on your answers to each of the questions and the code implementations you provide.

Steps to Complete the Project

Each part of the notebook is further broken down into separate steps. Feel free to use the links below to navigate the notebook.

In this project you will get to explore a few of the many computer vision algorithms built into the OpenCV library. This expansive computer vision library is now almost 20 years old and still growing!

The project itself is broken down into three large parts, then even further into separate steps. Make sure to read through each step, and complete any sections that begin with '(IMPLEMENTATION)' in the header; these implementation sections may contain multiple TODOs that will be marked in code. For convenience, we provide links to each of these steps below.

Part 1 : Investigating OpenCV, pre-processing, and face detection

  • Step 0: Detect Faces Using a Haar Cascade Classifier
  • Step 1: Add Eye Detection
  • Step 2: De-noise an Image for Better Face Detection
  • Step 3: Blur an Image and Perform Edge Detection
  • Step 4: Automatically Hide the Identity of an Individual

Part 2 : Training a Convolutional Neural Network (CNN) to detect facial keypoints

  • Step 5: Create a CNN to Recognize Facial Keypoints
  • Step 6: Compile and Train the Model
  • Step 7: Visualize the Loss and Answer Questions

Part 3 : Putting parts 1 and 2 together to identify facial keypoints on any image!

  • Step 8: Build a Robust Facial Keypoints Detector (Complete the CV Pipeline)

Step 0: Detect Faces Using a Haar Cascade Classifier

Have you ever wondered how Facebook automatically tags images with your friends' faces? Or how high-end cameras automatically find and focus on a certain person's face? Applications like these depend heavily on the machine learning task known as face detection - which is the task of automatically finding faces in images containing people.

At its root face detection is a classification problem - that is a problem of distinguishing between distinct classes of things. With face detection these distinct classes are 1) images of human faces and 2) everything else.

We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the detector_architectures directory.

Import Resources

In the next python cell, we load in the required libraries for this section of the project.

In [89]:
# Import required libraries for this section

%matplotlib inline

import numpy as np
import matplotlib.pyplot as plt
import math
import cv2                     # OpenCV library for computer vision
from PIL import Image
import time 

Next, we load in and display a test image for performing face detection.

Note: by default OpenCV assumes the ordering of our image's color channels are Blue, then Green, then Red. This is slightly out of order with most image types we'll use in these experiments, whose color channels are ordered Red, then Green, then Blue. In order to switch the Blue and Red channels of our test image around we will use OpenCV's cvtColor function, which you can read more about by checking out some of its documentation located here. This is a general utility function that can do other transformations too like converting a color image to grayscale, and transforming a standard color image to HSV color space.

In [90]:
# Load in color image for face detection
image = cv2.imread('images/test_image_1.jpg')

# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Plot our image using subplots to specify a size and title
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Original Image')
ax1.imshow(image)
Out[90]:
<matplotlib.image.AxesImage at 0x1fbcd4c25f8>

There are a lot of people - and faces - in this picture. 13 faces to be exact! In the next code cell, we demonstrate how to use a Haar Cascade classifier to detect all the faces in this test image.

This face detector uses information about patterns of intensity in an image to reliably detect faces under varying light conditions. So, to use this face detector, we'll first convert the image from color to grayscale.

Then, we load in the fully trained architecture of the face detector -- found in the file haarcascade_frontalface_default.xml - and use it on our image to find faces!

To learn more about the parameters of the detector see this post.

In [91]:
# Convert the RGB  image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# Detect the faces in image
faces = face_cascade.detectMultiScale(gray, 4, 6)

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))

# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(image)

# Get the bounding box for each detected face
for (x,y,w,h) in faces:
    # Add a red bounding box to the detections image
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
    

# Display the image with the detections
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Image with Face Detections')
ax1.imshow(image_with_detections)
Number of faces detected: 13
Out[91]:
<matplotlib.image.AxesImage at 0x1fbdc5232b0>

In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.


Step 1: Add Eye Detections

There are other pre-trained detectors available that use a Haar Cascade Classifier - including full human body detectors, license plate detectors, and more. A full list of the pre-trained architectures can be found here.

To test your eye detector, we'll first read in a new test image with just a single face.

In [92]:
# Load in color image for face detection
image = cv2.imread('images/james.jpg')

# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Plot the RGB image
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Original Image')
ax1.imshow(image)
Out[92]:
<matplotlib.image.AxesImage at 0x1fbd1eeb940>

Notice that even though the image is a black and white image, we have read it in as a color image and so it will still need to be converted to grayscale in order to perform the most accurate face detection.

So, the next steps will be to convert this image to grayscale, then load OpenCV's face detector and run it with parameters that detect this face accurately.

In [93]:
# Convert the RGB  image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# Detect the faces in image
faces = face_cascade.detectMultiScale(gray, 1.25, 6)

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))

# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(image)

# Get the bounding box for each detected face
for (x,y,w,h) in faces:
    # Add a red bounding box to the detections image
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
    

# Display the image with the detections
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Image with Face Detection')
ax1.imshow(image_with_detections)
Number of faces detected: 1
Out[93]:
<matplotlib.image.AxesImage at 0x1fbd2193f60>

(IMPLEMENTATION) Add an eye detector to the current face detection setup.

A Haar-cascade eye detector can be included in the same way that the face detector was and, in this first task, it will be your job to do just this.

To set up an eye detector, use the stored parameters of the eye cascade detector, called haarcascade_eye.xml, located in the detector_architectures subdirectory. In the next code cell, create your eye detector and store its detections.

A few notes before you get started:

First, make sure to give your loaded eye detector the variable name

eye_cascade

and give the list of eye regions you detect the variable name

eyes

Second, since we've already run the face detector over this image, you should only search for eyes within the rectangular face regions detected in faces. This will minimize false detections.

Lastly, once you've run your eye detector over the facial detection region, you should display the RGB image with both the face detection boxes (in red) and your eye detections (in green) to verify that everything works as expected.

In [94]:
# Make a copy of the original image to plot rectangle detections
image_with_detections = np.copy(image)   

# Loop over the detections and draw their corresponding face detection boxes
for (x,y,w,h) in faces:
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h),(255,0,0), 3)  
    
# Do not change the code above this comment!


## TODO: Add eye detection, using haarcascade_eye.xml, to the current face detector algorithm
## TODO: Loop over the eye detections and draw their corresponding boxes in green on image_with_detections


# Extract the pre-trained eye detector from an xml file
eye_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_eye.xml')

for (x,y,w,h) in faces:
    # Detect the eyes in image
    cropped_face_img = gray[y:y+h, x:x+w]
    eyes = eye_cascade.detectMultiScale(cropped_face_img, 1.1, 6)  #Reduced scale factor to 1.1

    for (x_eyes, y_eyes, w_eyes, h_eyes) in eyes:
        cv2.rectangle(image_with_detections, (x+x_eyes, y+y_eyes), (x+x_eyes+w_eyes,y+y_eyes+h_eyes),(0,255,0), 3)

    


# Plot the image with both faces and eyes detected
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Image with Face and Eye Detection')
ax1.imshow(image_with_detections)
Out[94]:
<matplotlib.image.AxesImage at 0x1fbd219b198>

(Optional) Add face and eye detection to your laptop camera

It's time to kick it up a notch, and add face and eye detection to your laptop's camera! Afterwards, you'll be able to show off your creation like in the gif shown below - made with a completed version of the code!

Notice that not all of the detections here are perfect - and your result need not be perfect either. You should spend a small amount of time tuning the parameters of your detectors to get reasonable results, but don't hold out for perfection. If we wanted perfection we'd need to spend a ton of time tuning the parameters of each detector, cleaning up the input image frames, etc. You can think of this as more of a rapid prototype.

The next cell contains code for a wrapper function called laptop_camera_face_eye_detector that, when called, will activate your laptop's camera. You will place the relevant face and eye detection code in this wrapper function to implement face/eye detection and mark those detections on each image frame that your camera captures.

Before adding anything to the function, you can run it to get an idea of how it works - a small window should pop up showing you the live feed from your camera; you can press any key to close this window.

Note: Mac users may find that activating this function kills the kernel of their notebook every once in a while. If this happens to you, just restart your notebook's kernel, activate cell(s) containing any crucial import statements, and you'll be good to go!

In [95]:
### Add face and eye detection to this laptop camera function 
# Make sure to draw out all faces/eyes found in each frame on the shown video feed

import cv2
import time 

def detect_face_eyes(image, face_cascade, eye_cascade):
    # Make a copy of the original image to plot rectangle detections
    image_with_detections = np.copy(image)   

    # Convert the RGB  image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    
    # Detect the faces in image
    faces = face_cascade.detectMultiScale(gray, 1.26, 5)
    
    # Loop over the detections and draw their corresponding face detection boxes
    for (x,y,w,h) in faces:
        cv2.rectangle(image_with_detections, (x,y), (x+w,y+h),(255,0,0), 3)  

    if eye_cascade:       
        for (x,y,w,h) in faces:
            # Detect the eyes in image
            cropped_face_img = gray[y:y+h, x:x+w]
            eyes = eye_cascade.detectMultiScale(cropped_face_img, 1.1, 6)  #Reduced scale factor to 1.1

            for (x_eyes, y_eyes, w_eyes, h_eyes) in eyes:
                cv2.rectangle(image_with_detections, (x+x_eyes, y+y_eyes), (x+x_eyes+w_eyes,y+y_eyes+h_eyes),(0,255,0), 3)

    return image_with_detections


# wrapper function for face/eye detection with your laptop camera
def laptop_camera_go():

    # Extract the pre-trained face detector from an xml file
    face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

    # Extract the pre-trained eye detector from an xml file
    eye_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_eye.xml')

        
    
    # Create instance of video capturer
    cv2.namedWindow("face detection activated")
    vc = cv2.VideoCapture(0)

    # Try to get the first frame
    if vc.isOpened(): 
        rval, frame = vc.read()
    else:
        rval = False
    
    # Keep the video stream open
    while rval:

        frame = detect_face_eyes(frame, face_cascade, eye_cascade)
    
        # Plot the image from camera with all the face and eye detections marked
        cv2.imshow("face detection activated", frame)
        
        # Exit functionality - press any key to exit laptop video
        key = cv2.waitKey(20)
        
        #Note: It seems for windows 255 is returned instead of -1 for key value
        if key > 0 and key != 255: # Exit by pressing any key
            # Destroy windows 
            cv2.destroyAllWindows()            
            # Make sure window closes on OSx
            for i in range (1,5):
                cv2.waitKey(1)
            return
        
        # Read next frame
        time.sleep(0.05)             # control framerate for computation - default 20 frames per sec
        rval, frame = vc.read()    
In [96]:
# Call the laptop camera face/eye detector function above
laptop_camera_go()

^ That was fun :)


Step 2: De-noise an Image for Better Face Detection

Image quality is an important aspect of any computer vision task. Typically, when creating a set of images to train a deep learning network, significant care is taken to ensure that training images are free of visual noise or artifacts that hinder object detection. While computer vision algorithms - like a face detector - are typically trained on 'nice' data such as this, new test data doesn't always look so nice!

When applying a trained computer vision algorithm to a new piece of test data one often cleans it up first before feeding it in. This sort of cleaning - referred to as pre-processing - can include a number of cleaning phases like blurring, de-noising, color transformations, etc., and many of these tasks can be accomplished using OpenCV.

In this short subsection we explore OpenCV's noise-removal functionality to see how we can clean up a noisy image, which we then feed into our trained face detector.

Create a noisy image to work with

In the next cell, we create an artificial noisy version of the previous multi-face image. This is a little exaggerated - we don't typically get images that are this noisy - but image noise, or 'grainy-ness' in a digitial image - is a fairly common phenomenon.

In [105]:
# Load in the multi-face test image again
image = cv2.imread('images/test_image_1.jpg')

# Convert the image copy to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Make an array copy of this image
image_with_noise = np.asarray(image)

# Create noise - here we add noise sampled randomly from a Gaussian distribution: a common model for noise
noise_level = 40
noise = np.random.randn(image.shape[0],image.shape[1],image.shape[2])*noise_level

# Add this noise to the array image copy
image_with_noise = image_with_noise + noise

# Convert back to uint8 format
image_with_noise = np.asarray([np.uint8(np.clip(i,0,255)) for i in image_with_noise])

# Plot our noisy image!
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Noisy Image')
ax1.imshow(image_with_noise)
Out[105]:
<matplotlib.image.AxesImage at 0x1fbe81b3080>

In the context of face detection, the problem with an image like this is that - due to noise - we may miss some faces or get false detections.

In the next cell we apply the same trained OpenCV detector with the same settings as before, to see what sort of detections we get.

In [106]:
# Convert the RGB  image to grayscale
gray_noise = cv2.cvtColor(image_with_noise, cv2.COLOR_RGB2GRAY)

# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# Detect the faces in image
faces = face_cascade.detectMultiScale(gray_noise, 4, 6)

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))

# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(image_with_noise)

# Get the bounding box for each detected face
for (x,y,w,h) in faces:
    # Add a red bounding box to the detections image
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
    

# Display the image with the detections
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Noisy Image with Face Detections')
ax1.imshow(image_with_detections)
Number of faces detected: 12
Out[106]:
<matplotlib.image.AxesImage at 0x1fbe681e630>

With this added noise we now miss one of the faces!

(IMPLEMENTATION) De-noise this image for better face detection

Time to get your hands dirty: using OpenCV's built in color image de-noising functionality called fastNlMeansDenoisingColored - de-noise this image enough so that all the faces in the image are properly detected. Once you have cleaned the image in the next cell, use the cell that follows to run our trained face detector over the cleaned image to check out its detections.

You can find its official documentation here and a useful example here.

Note: you can keep all parameters except photo_render fixed as shown in the second link above. Play around with the value of this parameter - see how it affects the resulting cleaned image.

In [109]:
## TODO: Use OpenCV's built in color image de-noising function to clean up our noisy image!

denoised_image = cv2.fastNlMeansDenoisingColored(image_with_noise,None,16,16,7,21)  #<-- Paramer were tuned here

# Display the image with the detections
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('De-noised image')
ax1.imshow(denoised_image)
Out[109]:
<matplotlib.image.AxesImage at 0x1fbe8373b00>
In [110]:
## TODO: Run the face detector on the de-noised image to improve your detections and display the result

# Convert the RGB  image to grayscale
gray_denoise = cv2.cvtColor(denoised_image, cv2.COLOR_RGB2GRAY)

# Detect the faces in image
faces = face_cascade.detectMultiScale(gray_denoise, 4, 6)   #Kept the same params here as before

# Print the number of faces detected in the image
print('Number of faces detected:', len(faces))

# Make a copy of the orginal image to draw face detections on
image_with_detections = np.copy(denoised_image)

# Get the bounding box for each detected face
for (x,y,w,h) in faces:
    # Add a red bounding box to the detections image
    cv2.rectangle(image_with_detections, (x,y), (x+w,y+h), (255,0,0), 3)
    

# Display the image with the detections
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('De-Noised Image with Face Detections')
ax1.imshow(image_with_detections)
Number of faces detected: 13
Out[110]:
<matplotlib.image.AxesImage at 0x1fbd1fee240>

Step 3: Blur an Image and Perform Edge Detection

Now that we have developed a simple pipeline for detecting faces using OpenCV - let's start playing around with a few fun things we can do with all those detected faces!

Importance of Blur in Edge Detection

Edge detection is a concept that pops up almost everywhere in computer vision applications, as edge-based features (as well as features built on top of edges) are often some of the best features for e.g., object detection and recognition problems.

Edge detection is a dimension reduction technique - by keeping only the edges of an image we get to throw away a lot of non-discriminating information. And typically the most useful kind of edge-detection is one that preserves only the important, global structures (ignoring local structures that aren't very discriminative). So removing local structures / retaining global structures is a crucial pre-processing step to performing edge detection in an image, and blurring can do just that.

Below is an animated gif showing the result of an edge-detected cat taken from Wikipedia, where the image is gradually blurred more and more prior to edge detection. When the animation begins you can't quite make out what it's a picture of, but as the animation evolves and local structures are removed via blurring the cat becomes visible in the edge-detected image.

Edge detection is a convolution performed on the image itself, and you can read about Canny edge detection on this OpenCV documentation page.

Canny edge detection

In the cell below we load in a test image, then apply Canny edge detection on it. The original image is shown on the left panel of the figure, while the edge-detected version of the image is shown on the right. Notice how the result looks very busy - there are too many little details preserved in the image before it is sent to the edge detector. When applied in computer vision applications, edge detection should preserve global structure; doing away with local structures that don't help describe what objects are in the image.

In [111]:
# Load in the image
image = cv2.imread('images/fawzia.jpg')

# Convert to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)  

# Perform Canny edge detection
edges = cv2.Canny(gray,100,200)

# Dilate the image to amplify edges
edges = cv2.dilate(edges, None)

# Plot the RGB and edge-detected image
fig = plt.figure(figsize = (15,15))
ax1 = fig.add_subplot(121)
ax1.set_xticks([])
ax1.set_yticks([])

ax1.set_title('Original Image')
ax1.imshow(image)

ax2 = fig.add_subplot(122)
ax2.set_xticks([])
ax2.set_yticks([])

ax2.set_title('Canny Edges')
ax2.imshow(edges, cmap='gray')
Out[111]:
<matplotlib.image.AxesImage at 0x1fbd1f4ccf8>

Without first blurring the image, and removing small, local structures, a lot of irrelevant edge content gets picked up and amplified by the detector (as shown in the right panel above).

(IMPLEMENTATION) Blur the image then perform edge detection

In the next cell, you will repeat this experiment - blurring the image first to remove these local structures, so that only the important boudnary details remain in the edge-detected image.

Blur the image by using OpenCV's filter2d functionality - which is discussed in this documentation page - and use an averaging kernel of width equal to 4.

In [112]:
### TODO: Blur the test imageusing OpenCV's filter2d functionality, 
# Use an averaging kernel, and a kernel width equal to 4

kernel = np.ones((4,4),np.float32)/16
gray_blurred = cv2.filter2D(gray,-1,kernel)


# Plot the Gray and blurred gray
fig = plt.figure(figsize = (15,15))

ax1 = fig.add_subplot(121)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Original Image')
ax1.imshow(gray, cmap='gray')

ax2 = fig.add_subplot(122)
ax2.set_xticks([])
ax2.set_yticks([])
ax2.set_title('Blurred Image')
ax2.imshow(gray_blurred, cmap='gray')


## TODO: Then perform Canny edge detection and display the output

# Perform Canny edge detection
edges = cv2.Canny(gray_blurred,100,200)

# Dilate the image to amplify edges
edges = cv2.dilate(edges, None)


# Plot the blurred gray and edges
fig = plt.figure(figsize = (7,7))

ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Edges after blur')
ax1.imshow(edges, cmap='gray')
Out[112]:
<matplotlib.image.AxesImage at 0x1fbd20e2d30>

Step 4: Automatically Hide the Identity of an Individual

If you film something like a documentary or reality TV, you must get permission from every individual shown on film before you can show their face, otherwise you need to blur it out - by blurring the face a lot (so much so that even the global structures are obscured)! This is also true for projects like Google's StreetView maps - an enormous collection of mapping images taken from a fleet of Google vehicles. Because it would be impossible for Google to get the permission of every single person accidentally captured in one of these images they blur out everyone's faces, the detected images must automatically blur the identity of detected people. Here's a few examples of folks caught in the camera of a Google street view vehicle.

Read in an image to perform identity detection

Let's try this out for ourselves. Use the face detection pipeline built above and what you know about using the filter2D to blur and image, and use these in tandem to hide the identity of the person in the following image - loaded in and printed in the next cell.

In [113]:
# Load in the image
image = cv2.imread('images/gus.jpg')

# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Display the image
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Original Image')
ax1.imshow(image)
Out[113]:
<matplotlib.image.AxesImage at 0x1fbe8489c50>

(IMPLEMENTATION) Use blurring to hide the identity of an individual in an image

The idea here is to 1) automatically detect the face in this image, and then 2) blur it out! Make sure to adjust the parameters of the averaging blur filter to completely obscure this person's identity.

In [114]:
## TODO: Implement face detection

def detect_face_effects(image, face_cascade, eye_cascade, showFaceRect=True, effect=None):
    # Make a copy of the original image to plot rectangle detections
    image_with_detections = np.copy(image)   

    # Convert the RGB  image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    
    # Detect the faces in image
    faces = face_cascade.detectMultiScale(gray, 1.26, 5)
    
    # Loop over the detections and draw their corresponding detection boxes
    for (x,y,w,h) in faces:
        
        if showFaceRect:            
            cv2.rectangle(image_with_detections, (x,y), (x+w,y+h),(255,0,0), 3)  
        
        if eye_cascade:
            # Detect the eyes in image
            cropped_face_img = gray[y:y+h, x:x+w]
            eyes = eye_cascade.detectMultiScale(cropped_face_img, 1.1, 6)  #Reduced scale factor to 1.1

            for (x_eyes, y_eyes, w_eyes, h_eyes) in eyes:
                cv2.rectangle(image_with_detections, (x+x_eyes, y+y_eyes), (x+x_eyes+w_eyes,y+y_eyes+h_eyes),(0,255,0), 3)
        
        if effect == "blur":
            cropped_face_img = image_with_detections[y:y+h, x:x+w]
            kernel = np.ones((50,50),np.float32)/2500
            cropped_face_img_blurred = cv2.filter2D(cropped_face_img,-1,kernel)
            image_with_detections[y:y+h, x:x+w] = cropped_face_img_blurred
                
    return image_with_detections


# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# Extract the pre-trained eye detector from an xml file
eye_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_eye.xml')

image_face_detected = detect_face_effects(image, face_cascade, None, showFaceRect=False, effect="blur")

# Display the image
fig = plt.figure(figsize = (10,10))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Detected Face')
ax1.imshow(image_face_detected)


## TODO: Blur the bounding box around each detected face using an averaging filter and display the result
Out[114]:
<matplotlib.image.AxesImage at 0x1fbe84c3d30>

(Optional) Build identity protection into your laptop camera

In this optional task you can add identity protection to your laptop camera, using the previously completed code where you added face detection to your laptop camera - and the task above. You should be able to get reasonable results with little parameter tuning - like the one shown in the gif below.

As with the previous video task, to make this perfect would require significant effort - so don't strive for perfection here, strive for reasonable quality.

The next cell contains code a wrapper function called laptop_camera_identity_hider that - when called - will activate your laptop's camera. You need to place the relevant face detection and blurring code developed above in this function in order to blur faces entering your laptop camera's field of view.

Before adding anything to the function you can call it to get a hang of how it works - a small window will pop up showing you the live feed from your camera, you can press any key to close this window.

Note: Mac users may find that activating this function kills the kernel of their notebook every once in a while. If this happens to you, just restart your notebook's kernel, activate cell(s) containing any crucial import statements, and you'll be good to go!

In [115]:
### Insert face detection and blurring code into the wrapper below to create an identity protector on your laptop!
import cv2
import time 

def laptop_camera_go():
    # Create instance of video capturer
    cv2.namedWindow("face detection activated")
    vc = cv2.VideoCapture(0)

    # Try to get the first frame
    if vc.isOpened(): 
        rval, frame = vc.read()
    else:
        rval = False
    
    # Keep video stream open
    while rval:
        
        #Function implemented in earlier cell
        frame = detect_face_effects(frame, face_cascade, None, showFaceRect=False, effect="blur")
        
        # Plot image from camera with detections marked
        cv2.imshow("face detection activated", frame)
        
        # Exit functionality - press any key to exit laptop video
        key = cv2.waitKey(20)
        if key > 0 and key != 255: # Exit by pressing any key
            # Destroy windows
            cv2.destroyAllWindows()
            
            for i in range (1,5):
                cv2.waitKey(1)
            return
        
        # Read next frame
        time.sleep(0.05)             # control framerate for computation - default 20 frames per sec
        rval, frame = vc.read()    
        
In [116]:
# Run laptop identity hider
laptop_camera_go()

Step 5: Create a CNN to Recognize Facial Keypoints

OpenCV is often used in practice with other machine learning and deep learning libraries to produce interesting results. In this stage of the project you will create your own end-to-end pipeline - employing convolutional networks in keras along with OpenCV - to apply a "selfie" filter to streaming video and images.

You will start by creating and then training a convolutional network that can detect facial keypoints in a small dataset of cropped images of human faces. We then guide you towards OpenCV to expanding your detection algorithm to more general images. What are facial keypoints? Let's take a look at some examples.

Facial keypoints (also called facial landmarks) are the small blue-green dots shown on each of the faces in the image above - there are 15 keypoints marked in each image. They mark important areas of the face - the eyes, corners of the mouth, the nose, etc. Facial keypoints can be used in a variety of machine learning applications from face and emotion recognition to commercial applications like the image filters popularized by Snapchat.

Below we illustrate a filter that, using the results of this section, automatically places sunglasses on people in images (using the facial keypoints to place the glasses correctly on each face). Here, the facial keypoints have been colored lime green for visualization purposes.

Make a facial keypoint detector

But first things first: how can we make a facial keypoint detector? Well, at a high level, notice that facial keypoint detection is a regression problem. A single face corresponds to a set of 15 facial keypoints (a set of 15 corresponding $(x, y)$ coordinates, i.e., an output point). Because our input data are images, we can employ a convolutional neural network to recognize patterns in our images and learn how to identify these keypoint given sets of labeled data.

In order to train a regressor, we need a training set - a set of facial image / facial keypoint pairs to train on. For this we will be using this dataset from Kaggle. We've already downloaded this data and placed it in the data directory. Make sure that you have both the training and test data files. The training dataset contains several thousand $96 \times 96$ grayscale images of cropped human faces, along with each face's 15 corresponding facial keypoints (also called landmarks) that have been placed by hand, and recorded in $(x, y)$ coordinates. This wonderful resource also has a substantial testing set, which we will use in tinkering with our convolutional network.

To load in this data, run the Python cell below - notice we will load in both the training and testing sets.

The load_data function is in the included utils.py file.

In [2]:
from utils import *

# Load training set
X_train, y_train = load_data()
print("X_train.shape == {}".format(X_train.shape))
print("y_train.shape == {}; y_train.min == {:.3f}; y_train.max == {:.3f}".format(
    y_train.shape, y_train.min(), y_train.max()))

# Load testing set
X_test, _ = load_data(test=True)
print("X_test.shape == {}".format(X_test.shape))
X_train.shape == (2140, 96, 96, 1)
y_train.shape == (2140, 30); y_train.min == -0.920; y_train.max == 0.996
X_test.shape == (1783, 96, 96, 1)

The load_data function in utils.py originates from this excellent blog post, which you are strongly encouraged to read. Please take the time now to review this function. Note how the output values - that is, the coordinates of each set of facial landmarks - have been normalized to take on values in the range $[-1, 1]$, while the pixel values of each input point (a facial image) have been normalized to the range $[0,1]$.

Note: the original Kaggle dataset contains some images with several missing keypoints. For simplicity, the load_data function removes those images with missing labels from the dataset. As an optional extension, you are welcome to amend the load_data function to include the incomplete data points.

Visualize the Training Data

Execute the code cell below to visualize a subset of the training data.

In [3]:
import matplotlib.pyplot as plt
%matplotlib inline

fig = plt.figure(figsize=(20,20))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
for i in range(9):
    ax = fig.add_subplot(3, 3, i + 1, xticks=[], yticks=[])
    plot_data(X_train[i], y_train[i], ax)

For each training image, there are two landmarks per eyebrow (four total), three per eye (six total), four for the mouth, and one for the tip of the nose.

Review the plot_data function in utils.py to understand how the 30-dimensional training labels in y_train are mapped to facial locations, as this function will prove useful for your pipeline.

(IMPLEMENTATION) Specify the CNN Architecture

In this section, you will specify a neural network for predicting the locations of facial keypoints. Use the code cell below to specify the architecture of your neural network. We have imported some layers that you may find useful for this task, but if you need to use more Keras layers, feel free to import them in the cell.

Your network should accept a $96 \times 96$ grayscale image as input, and it should output a vector with 30 entries, corresponding to the predicted (horizontal and vertical) locations of 15 facial keypoints. If you are not sure where to start, you can find some useful starting architectures in this blog, but you are not permitted to copy any of the architectures that you find online.

In [79]:
# Import deep learning resources from Keras
from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D, Dropout
from keras.layers import Flatten, Dense


## TODO: Specify a CNN architecture
# Your model should accept 96x96 pixel graysale images in
# It should have a fully-connected output layer with 30 values (2 for each facial keypoint)


def get_model():   
    model = Sequential()


    model.add(Convolution2D(filters=16, kernel_size=3, input_shape=(96, 96, 1), activation='relu', padding='same'))
    model.add(MaxPooling2D(pool_size=2))
    model.add(Convolution2D(filters=32, kernel_size=3, activation='relu', padding='same'))
    model.add(MaxPooling2D(pool_size=2))
    model.add(Convolution2D(filters=64, kernel_size=3, activation='relu', padding='same'))
    model.add(MaxPooling2D(pool_size=2))
    #model.add(Convolution2D(filters=128, kernel_size=3, activation='relu', padding='same'))
    #model.add(MaxPooling2D(pool_size=2))
    #model.add(Convolution2D(filters=256, kernel_size=3, activation='relu', padding='same'))
    #model.add(MaxPooling2D(pool_size=2))
    #model.add(Convolution2D(filters=512, kernel_size=3, activation='relu', padding='same'))
    #model.add(MaxPooling2D(pool_size=2))
    model.add(Flatten())
    model.add(Dense(64, activation='relu'))
    model.add(Dropout(0.1))
    model.add(Dense(30))

    return model


model = get_model()

# Summarize the model
model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_94 (Conv2D)           (None, 96, 96, 16)        160       
_________________________________________________________________
max_pooling2d_94 (MaxPooling (None, 48, 48, 16)        0         
_________________________________________________________________
conv2d_95 (Conv2D)           (None, 48, 48, 32)        4640      
_________________________________________________________________
max_pooling2d_95 (MaxPooling (None, 24, 24, 32)        0         
_________________________________________________________________
conv2d_96 (Conv2D)           (None, 24, 24, 64)        18496     
_________________________________________________________________
max_pooling2d_96 (MaxPooling (None, 12, 12, 64)        0         
_________________________________________________________________
flatten_27 (Flatten)         (None, 9216)              0         
_________________________________________________________________
dense_53 (Dense)             (None, 64)                589888    
_________________________________________________________________
dropout_26 (Dropout)         (None, 64)                0         
_________________________________________________________________
dense_54 (Dense)             (None, 30)                1950      
=================================================================
Total params: 615,134
Trainable params: 615,134
Non-trainable params: 0
_________________________________________________________________

Step 6: Compile and Train the Model

After specifying your architecture, you'll need to compile and train the model to detect facial keypoints'

(IMPLEMENTATION) Compile and Train the Model

Use the compile method to configure the learning process. Experiment with your choice of optimizer; you may have some ideas about which will work best (SGD vs. RMSprop, etc), but take the time to empirically verify your theories.

Use the fit method to train the model. Break off a validation set by setting validation_split=0.2. Save the returned History object in the history variable.

Experiment with your model to minimize the validation loss (measured as mean squared error). A very good model will achieve about 0.0015 loss (though it's possible to do even better). When you have finished training, save your model as an HDF5 file with file path my_model.h5.

In [80]:
from keras.optimizers import SGD, RMSprop, Adagrad, Adadelta, Adam, Adamax, Nadam
from keras.callbacks import ModelCheckpoint


## Optimizers
optimizers = [SGD(), RMSprop(), Adagrad(), Adadelta(), Adam(), Adamax(), Nadam()]
names = ["SGD", "RMSprop", "Adagrad", "Adadelta", "Adam", "Adamax", "Nadam"]


## TODO: Compile the model



epochs = 80
batch_size = 20

histories = {}
for optimizer, optimizer_name in zip(optimizers, names):  
    model = get_model()

    print("------------------->Evaluating " + optimizer_name + " <----------------------------------------")
    print("-----------------------------------------------------------------------------")
    model.compile(optimizer = optimizer, loss = 'mean_squared_error', metrics = ['accuracy'])
    checkpointer = ModelCheckpoint(filepath = "my_model_" + optimizer_name + ".h5", verbose = 1, save_best_only = True)
    hist = model.fit(X_train, y_train, validation_split = 0.2, epochs = epochs, batch_size = batch_size, callbacks = [checkpointer], verbose = 1, shuffle=True)
    histories[optimizer_name] = hist



selected_optimizer = 'Adam'
model.load_weights("my_model_" + selected_optimizer + ".h5")

## TODO: Save the model as model.h5
model.save('my_model.h5')
------------------->Evaluating SGD <----------------------------------------
-----------------------------------------------------------------------------
Train on 1712 samples, validate on 428 samples
Epoch 1/80
1712/1712 [==============================] - 2s 1ms/step - loss: 0.0634 - acc: 0.4276 - val_loss: 0.0283 - val_acc: 0.6425

Epoch 00001: val_loss improved from inf to 0.02830, saving model to my_model_SGD.h5
Epoch 2/80
1712/1712 [==============================] - 1s 423us/step - loss: 0.0338 - acc: 0.4264 - val_loss: 0.0169 - val_acc: 0.5631

Epoch 00002: val_loss improved from 0.02830 to 0.01691, saving model to my_model_SGD.h5
Epoch 3/80
1712/1712 [==============================] - 1s 419us/step - loss: 0.0260 - acc: 0.4591 - val_loss: 0.0122 - val_acc: 0.6589

Epoch 00003: val_loss improved from 0.01691 to 0.01223, saving model to my_model_SGD.h5
Epoch 4/80
1712/1712 [==============================] - 1s 430us/step - loss: 0.0227 - acc: 0.4690 - val_loss: 0.0105 - val_acc: 0.6799

Epoch 00004: val_loss improved from 0.01223 to 0.01054, saving model to my_model_SGD.h5
Epoch 5/80
1712/1712 [==============================] - 1s 416us/step - loss: 0.0200 - acc: 0.5082 - val_loss: 0.0094 - val_acc: 0.6846

Epoch 00005: val_loss improved from 0.01054 to 0.00944, saving model to my_model_SGD.h5
Epoch 6/80
1712/1712 [==============================] - 1s 427us/step - loss: 0.0187 - acc: 0.4982 - val_loss: 0.0084 - val_acc: 0.6869

Epoch 00006: val_loss improved from 0.00944 to 0.00844, saving model to my_model_SGD.h5
Epoch 7/80
1712/1712 [==============================] - 1s 425us/step - loss: 0.0177 - acc: 0.5053 - val_loss: 0.0078 - val_acc: 0.6846

Epoch 00007: val_loss improved from 0.00844 to 0.00780, saving model to my_model_SGD.h5
Epoch 8/80
1712/1712 [==============================] - 1s 422us/step - loss: 0.0166 - acc: 0.4883 - val_loss: 0.0070 - val_acc: 0.6893

Epoch 00008: val_loss improved from 0.00780 to 0.00695, saving model to my_model_SGD.h5
Epoch 9/80
1712/1712 [==============================] - 1s 410us/step - loss: 0.0153 - acc: 0.5275 - val_loss: 0.0071 - val_acc: 0.6893

Epoch 00009: val_loss did not improve
Epoch 10/80
1712/1712 [==============================] - 1s 420us/step - loss: 0.0150 - acc: 0.5099 - val_loss: 0.0069 - val_acc: 0.6893

Epoch 00010: val_loss improved from 0.00695 to 0.00685, saving model to my_model_SGD.h5
Epoch 11/80
1712/1712 [==============================] - 1s 436us/step - loss: 0.0144 - acc: 0.5006 - val_loss: 0.0064 - val_acc: 0.6893

Epoch 00011: val_loss improved from 0.00685 to 0.00637, saving model to my_model_SGD.h5
Epoch 12/80
1712/1712 [==============================] - 1s 416us/step - loss: 0.0136 - acc: 0.5117 - val_loss: 0.0063 - val_acc: 0.6893

Epoch 00012: val_loss improved from 0.00637 to 0.00633, saving model to my_model_SGD.h5
Epoch 13/80
1712/1712 [==============================] - 1s 418us/step - loss: 0.0132 - acc: 0.5134 - val_loss: 0.0057 - val_acc: 0.6893

Epoch 00013: val_loss improved from 0.00633 to 0.00574, saving model to my_model_SGD.h5
Epoch 14/80
1712/1712 [==============================] - 1s 424us/step - loss: 0.0127 - acc: 0.5175 - val_loss: 0.0056 - val_acc: 0.6893

Epoch 00014: val_loss improved from 0.00574 to 0.00559, saving model to my_model_SGD.h5
Epoch 15/80
1712/1712 [==============================] - 1s 425us/step - loss: 0.0125 - acc: 0.5029 - val_loss: 0.0057 - val_acc: 0.6916

Epoch 00015: val_loss did not improve
Epoch 16/80
1712/1712 [==============================] - 1s 414us/step - loss: 0.0119 - acc: 0.5216 - val_loss: 0.0053 - val_acc: 0.6916

Epoch 00016: val_loss improved from 0.00559 to 0.00525, saving model to my_model_SGD.h5
Epoch 17/80
1712/1712 [==============================] - 1s 420us/step - loss: 0.0120 - acc: 0.5158 - val_loss: 0.0055 - val_acc: 0.6939

Epoch 00017: val_loss did not improve
Epoch 18/80
1712/1712 [==============================] - 1s 421us/step - loss: 0.0116 - acc: 0.5228 - val_loss: 0.0052 - val_acc: 0.6916

Epoch 00018: val_loss improved from 0.00525 to 0.00520, saving model to my_model_SGD.h5
Epoch 19/80
1712/1712 [==============================] - 1s 419us/step - loss: 0.0114 - acc: 0.5298 - val_loss: 0.0051 - val_acc: 0.6916

Epoch 00019: val_loss improved from 0.00520 to 0.00505, saving model to my_model_SGD.h5
Epoch 20/80
1712/1712 [==============================] - 1s 425us/step - loss: 0.0111 - acc: 0.5321 - val_loss: 0.0049 - val_acc: 0.6916

Epoch 00020: val_loss improved from 0.00505 to 0.00492, saving model to my_model_SGD.h5
Epoch 21/80
1712/1712 [==============================] - 1s 414us/step - loss: 0.0107 - acc: 0.5327 - val_loss: 0.0051 - val_acc: 0.6916

Epoch 00021: val_loss did not improve
Epoch 22/80
1712/1712 [==============================] - 1s 433us/step - loss: 0.0105 - acc: 0.5304 - val_loss: 0.0049 - val_acc: 0.6916

Epoch 00022: val_loss did not improve
Epoch 23/80
1712/1712 [==============================] - 1s 412us/step - loss: 0.0107 - acc: 0.5251 - val_loss: 0.0048 - val_acc: 0.6916

Epoch 00023: val_loss improved from 0.00492 to 0.00475, saving model to my_model_SGD.h5
Epoch 24/80
1712/1712 [==============================] - 1s 410us/step - loss: 0.0102 - acc: 0.5333 - val_loss: 0.0048 - val_acc: 0.6916

Epoch 00024: val_loss did not improve
Epoch 25/80
1712/1712 [==============================] - 1s 413us/step - loss: 0.0102 - acc: 0.5543 - val_loss: 0.0049 - val_acc: 0.6916

Epoch 00025: val_loss did not improve
Epoch 26/80
1712/1712 [==============================] - 1s 419us/step - loss: 0.0099 - acc: 0.5315 - val_loss: 0.0048 - val_acc: 0.6916

Epoch 00026: val_loss did not improve
Epoch 27/80
1712/1712 [==============================] - 1s 431us/step - loss: 0.0099 - acc: 0.5456 - val_loss: 0.0046 - val_acc: 0.6916

Epoch 00027: val_loss improved from 0.00475 to 0.00464, saving model to my_model_SGD.h5
Epoch 28/80
1712/1712 [==============================] - 1s 413us/step - loss: 0.0099 - acc: 0.5210 - val_loss: 0.0047 - val_acc: 0.6916

Epoch 00028: val_loss did not improve
Epoch 29/80
1712/1712 [==============================] - 1s 426us/step - loss: 0.0096 - acc: 0.5228 - val_loss: 0.0046 - val_acc: 0.6916

Epoch 00029: val_loss improved from 0.00464 to 0.00460, saving model to my_model_SGD.h5
Epoch 30/80
1712/1712 [==============================] - 1s 425us/step - loss: 0.0096 - acc: 0.5438 - val_loss: 0.0046 - val_acc: 0.6916

Epoch 00030: val_loss did not improve
Epoch 31/80
1712/1712 [==============================] - 1s 416us/step - loss: 0.0094 - acc: 0.5508 - val_loss: 0.0046 - val_acc: 0.6916

Epoch 00031: val_loss did not improve
Epoch 32/80
1712/1712 [==============================] - 1s 415us/step - loss: 0.0094 - acc: 0.5409 - val_loss: 0.0047 - val_acc: 0.6916

Epoch 00032: val_loss did not improve
Epoch 33/80
1712/1712 [==============================] - 1s 424us/step - loss: 0.0092 - acc: 0.5450 - val_loss: 0.0047 - val_acc: 0.6916

Epoch 00033: val_loss did not improve
Epoch 34/80
1712/1712 [==============================] - 1s 419us/step - loss: 0.0091 - acc: 0.5584 - val_loss: 0.0045 - val_acc: 0.6916

Epoch 00034: val_loss improved from 0.00460 to 0.00455, saving model to my_model_SGD.h5
Epoch 35/80
1712/1712 [==============================] - 1s 414us/step - loss: 0.0092 - acc: 0.5572 - val_loss: 0.0047 - val_acc: 0.6916

Epoch 00035: val_loss did not improve
Epoch 36/80
1712/1712 [==============================] - 1s 416us/step - loss: 0.0090 - acc: 0.5572 - val_loss: 0.0046 - val_acc: 0.6916

Epoch 00036: val_loss did not improve
Epoch 37/80
1712/1712 [==============================] - 1s 413us/step - loss: 0.0090 - acc: 0.5350 - val_loss: 0.0045 - val_acc: 0.6916

Epoch 00037: val_loss improved from 0.00455 to 0.00448, saving model to my_model_SGD.h5
Epoch 38/80
1712/1712 [==============================] - 1s 421us/step - loss: 0.0089 - acc: 0.5526 - val_loss: 0.0046 - val_acc: 0.6916

Epoch 00038: val_loss did not improve
Epoch 39/80
1712/1712 [==============================] - 1s 408us/step - loss: 0.0086 - acc: 0.5578 - val_loss: 0.0046 - val_acc: 0.6916

Epoch 00039: val_loss did not improve
Epoch 40/80
1712/1712 [==============================] - 1s 411us/step - loss: 0.0089 - acc: 0.5613 - val_loss: 0.0045 - val_acc: 0.6916

Epoch 00040: val_loss did not improve
Epoch 41/80
1712/1712 [==============================] - 1s 421us/step - loss: 0.0088 - acc: 0.5572 - val_loss: 0.0046 - val_acc: 0.6916

Epoch 00041: val_loss did not improve
Epoch 42/80
1712/1712 [==============================] - 1s 419us/step - loss: 0.0084 - acc: 0.5648 - val_loss: 0.0046 - val_acc: 0.6916

Epoch 00042: val_loss did not improve
Epoch 43/80
1712/1712 [==============================] - 1s 418us/step - loss: 0.0085 - acc: 0.5537 - val_loss: 0.0044 - val_acc: 0.6916

Epoch 00043: val_loss improved from 0.00448 to 0.00444, saving model to my_model_SGD.h5
Epoch 44/80
1712/1712 [==============================] - 1s 423us/step - loss: 0.0083 - acc: 0.5730 - val_loss: 0.0045 - val_acc: 0.6916

Epoch 00044: val_loss did not improve
Epoch 45/80
1712/1712 [==============================] - 1s 420us/step - loss: 0.0083 - acc: 0.5672 - val_loss: 0.0045 - val_acc: 0.6916

Epoch 00045: val_loss did not improve
Epoch 46/80
1712/1712 [==============================] - 1s 414us/step - loss: 0.0086 - acc: 0.5596 - val_loss: 0.0044 - val_acc: 0.6916

Epoch 00046: val_loss did not improve
Epoch 47/80
1712/1712 [==============================] - 1s 414us/step - loss: 0.0082 - acc: 0.5864 - val_loss: 0.0045 - val_acc: 0.6916

Epoch 00047: val_loss did not improve
Epoch 48/80
1712/1712 [==============================] - 1s 407us/step - loss: 0.0082 - acc: 0.5543 - val_loss: 0.0045 - val_acc: 0.6916

Epoch 00048: val_loss did not improve
Epoch 49/80
1712/1712 [==============================] - 1s 414us/step - loss: 0.0082 - acc: 0.5718 - val_loss: 0.0045 - val_acc: 0.6916

Epoch 00049: val_loss did not improve
Epoch 50/80
1712/1712 [==============================] - 1s 407us/step - loss: 0.0081 - acc: 0.5619 - val_loss: 0.0044 - val_acc: 0.6916

Epoch 00050: val_loss improved from 0.00444 to 0.00439, saving model to my_model_SGD.h5
Epoch 51/80
1712/1712 [==============================] - 1s 418us/step - loss: 0.0083 - acc: 0.5532 - val_loss: 0.0044 - val_acc: 0.6916

Epoch 00051: val_loss did not improve
Epoch 52/80
1712/1712 [==============================] - 1s 415us/step - loss: 0.0081 - acc: 0.5660 - val_loss: 0.0044 - val_acc: 0.6916

Epoch 00052: val_loss improved from 0.00439 to 0.00438, saving model to my_model_SGD.h5
Epoch 53/80
1712/1712 [==============================] - 1s 417us/step - loss: 0.0079 - acc: 0.5824 - val_loss: 0.0043 - val_acc: 0.6916

Epoch 00053: val_loss improved from 0.00438 to 0.00434, saving model to my_model_SGD.h5
Epoch 54/80
1712/1712 [==============================] - 1s 416us/step - loss: 0.0081 - acc: 0.5870 - val_loss: 0.0043 - val_acc: 0.6916

Epoch 00054: val_loss improved from 0.00434 to 0.00432, saving model to my_model_SGD.h5
Epoch 55/80
1712/1712 [==============================] - 1s 434us/step - loss: 0.0080 - acc: 0.5695 - val_loss: 0.0044 - val_acc: 0.6916

Epoch 00055: val_loss did not improve
Epoch 56/80
1712/1712 [==============================] - 1s 431us/step - loss: 0.0078 - acc: 0.5555 - val_loss: 0.0043 - val_acc: 0.6916

Epoch 00056: val_loss did not improve
Epoch 57/80
1712/1712 [==============================] - 1s 420us/step - loss: 0.0078 - acc: 0.5771 - val_loss: 0.0042 - val_acc: 0.6916

Epoch 00057: val_loss improved from 0.00432 to 0.00425, saving model to my_model_SGD.h5
Epoch 58/80
1712/1712 [==============================] - 1s 416us/step - loss: 0.0079 - acc: 0.5561 - val_loss: 0.0045 - val_acc: 0.6916

Epoch 00058: val_loss did not improve
Epoch 59/80
1712/1712 [==============================] - 1s 406us/step - loss: 0.0078 - acc: 0.5794 - val_loss: 0.0043 - val_acc: 0.6916

Epoch 00059: val_loss did not improve
Epoch 60/80
1712/1712 [==============================] - 1s 421us/step - loss: 0.0078 - acc: 0.5759 - val_loss: 0.0045 - val_acc: 0.6916

Epoch 00060: val_loss did not improve
Epoch 61/80
1712/1712 [==============================] - 1s 425us/step - loss: 0.0077 - acc: 0.5765 - val_loss: 0.0043 - val_acc: 0.6916

Epoch 00061: val_loss did not improve
Epoch 62/80
1712/1712 [==============================] - 1s 419us/step - loss: 0.0076 - acc: 0.5800 - val_loss: 0.0044 - val_acc: 0.6916

Epoch 00062: val_loss did not improve
Epoch 63/80
1712/1712 [==============================] - 1s 415us/step - loss: 0.0076 - acc: 0.5859 - val_loss: 0.0043 - val_acc: 0.6916

Epoch 00063: val_loss did not improve
Epoch 64/80
1712/1712 [==============================] - 1s 421us/step - loss: 0.0075 - acc: 0.5935 - val_loss: 0.0042 - val_acc: 0.6916

Epoch 00064: val_loss improved from 0.00425 to 0.00422, saving model to my_model_SGD.h5
Epoch 65/80
1712/1712 [==============================] - 1s 419us/step - loss: 0.0077 - acc: 0.5660 - val_loss: 0.0042 - val_acc: 0.6916

Epoch 00065: val_loss did not improve
Epoch 66/80
1712/1712 [==============================] - 1s 417us/step - loss: 0.0074 - acc: 0.6075 - val_loss: 0.0044 - val_acc: 0.6916

Epoch 00066: val_loss did not improve
Epoch 67/80
1712/1712 [==============================] - 1s 419us/step - loss: 0.0076 - acc: 0.5929 - val_loss: 0.0043 - val_acc: 0.6916

Epoch 00067: val_loss did not improve
Epoch 68/80
1712/1712 [==============================] - 1s 426us/step - loss: 0.0076 - acc: 0.5783 - val_loss: 0.0043 - val_acc: 0.6916

Epoch 00068: val_loss did not improve
Epoch 69/80
1712/1712 [==============================] - 1s 416us/step - loss: 0.0075 - acc: 0.5812 - val_loss: 0.0042 - val_acc: 0.6916

Epoch 00069: val_loss did not improve
Epoch 70/80
1712/1712 [==============================] - 1s 426us/step - loss: 0.0072 - acc: 0.5905 - val_loss: 0.0042 - val_acc: 0.6916

Epoch 00070: val_loss improved from 0.00422 to 0.00420, saving model to my_model_SGD.h5
Epoch 71/80
1712/1712 [==============================] - 1s 415us/step - loss: 0.0074 - acc: 0.5958 - val_loss: 0.0042 - val_acc: 0.6916

Epoch 00071: val_loss did not improve
Epoch 72/80
1712/1712 [==============================] - 1s 415us/step - loss: 0.0073 - acc: 0.5923 - val_loss: 0.0043 - val_acc: 0.6916

Epoch 00072: val_loss did not improve
Epoch 73/80
1712/1712 [==============================] - 1s 417us/step - loss: 0.0073 - acc: 0.5824 - val_loss: 0.0043 - val_acc: 0.6916

Epoch 00073: val_loss did not improve
Epoch 74/80
1712/1712 [==============================] - 1s 421us/step - loss: 0.0072 - acc: 0.5847 - val_loss: 0.0043 - val_acc: 0.6916

Epoch 00074: val_loss did not improve
Epoch 75/80
1712/1712 [==============================] - 1s 420us/step - loss: 0.0073 - acc: 0.5958 - val_loss: 0.0043 - val_acc: 0.6916

Epoch 00075: val_loss did not improve
Epoch 76/80
1712/1712 [==============================] - 1s 416us/step - loss: 0.0073 - acc: 0.5970 - val_loss: 0.0042 - val_acc: 0.6916

Epoch 00076: val_loss improved from 0.00420 to 0.00417, saving model to my_model_SGD.h5
Epoch 77/80
1712/1712 [==============================] - 1s 414us/step - loss: 0.0071 - acc: 0.5888 - val_loss: 0.0042 - val_acc: 0.6916

Epoch 00077: val_loss did not improve
Epoch 78/80
1712/1712 [==============================] - 1s 416us/step - loss: 0.0072 - acc: 0.5993 - val_loss: 0.0042 - val_acc: 0.6916

Epoch 00078: val_loss did not improve
Epoch 79/80
1712/1712 [==============================] - 1s 424us/step - loss: 0.0071 - acc: 0.5894 - val_loss: 0.0042 - val_acc: 0.6916

Epoch 00079: val_loss did not improve
Epoch 80/80
1712/1712 [==============================] - 1s 428us/step - loss: 0.0071 - acc: 0.5730 - val_loss: 0.0042 - val_acc: 0.6916

Epoch 00080: val_loss did not improve
------------------->Evaluating RMSprop <----------------------------------------
-----------------------------------------------------------------------------
Train on 1712 samples, validate on 428 samples
Epoch 1/80
1712/1712 [==============================] - 2s 1ms/step - loss: 0.0417 - acc: 0.3551 - val_loss: 0.0091 - val_acc: 0.6963

Epoch 00001: val_loss improved from inf to 0.00905, saving model to my_model_RMSprop.h5
Epoch 2/80
1712/1712 [==============================] - 1s 445us/step - loss: 0.0118 - acc: 0.5064 - val_loss: 0.0161 - val_acc: 0.6963

Epoch 00002: val_loss did not improve
Epoch 3/80
1712/1712 [==============================] - 1s 446us/step - loss: 0.0081 - acc: 0.5765 - val_loss: 0.0044 - val_acc: 0.6963

Epoch 00003: val_loss improved from 0.00905 to 0.00444, saving model to my_model_RMSprop.h5
Epoch 4/80
1712/1712 [==============================] - 1s 451us/step - loss: 0.0067 - acc: 0.6075 - val_loss: 0.0041 - val_acc: 0.6986

Epoch 00004: val_loss improved from 0.00444 to 0.00410, saving model to my_model_RMSprop.h5
Epoch 5/80
1712/1712 [==============================] - 1s 445us/step - loss: 0.0056 - acc: 0.6542 - val_loss: 0.0063 - val_acc: 0.6986

Epoch 00005: val_loss did not improve
Epoch 6/80
1712/1712 [==============================] - 1s 440us/step - loss: 0.0049 - acc: 0.6706 - val_loss: 0.0069 - val_acc: 0.6589

Epoch 00006: val_loss did not improve
Epoch 7/80
1712/1712 [==============================] - 1s 438us/step - loss: 0.0044 - acc: 0.6764 - val_loss: 0.0028 - val_acc: 0.6729

Epoch 00007: val_loss improved from 0.00410 to 0.00281, saving model to my_model_RMSprop.h5
Epoch 8/80
1712/1712 [==============================] - 1s 447us/step - loss: 0.0040 - acc: 0.6624 - val_loss: 0.0027 - val_acc: 0.7079

Epoch 00008: val_loss improved from 0.00281 to 0.00270, saving model to my_model_RMSprop.h5
Epoch 9/80
1712/1712 [==============================] - 1s 430us/step - loss: 0.0036 - acc: 0.6910 - val_loss: 0.0026 - val_acc: 0.7196

Epoch 00009: val_loss improved from 0.00270 to 0.00259, saving model to my_model_RMSprop.h5
Epoch 10/80
1712/1712 [==============================] - 1s 441us/step - loss: 0.0034 - acc: 0.7009 - val_loss: 0.0026 - val_acc: 0.6939

Epoch 00010: val_loss improved from 0.00259 to 0.00257, saving model to my_model_RMSprop.h5
Epoch 11/80
1712/1712 [==============================] - 1s 435us/step - loss: 0.0030 - acc: 0.7225 - val_loss: 0.0036 - val_acc: 0.6963

Epoch 00011: val_loss did not improve
Epoch 12/80
1712/1712 [==============================] - 1s 438us/step - loss: 0.0030 - acc: 0.6945 - val_loss: 0.0021 - val_acc: 0.7079

Epoch 00012: val_loss improved from 0.00257 to 0.00212, saving model to my_model_RMSprop.h5
Epoch 13/80
1712/1712 [==============================] - 1s 440us/step - loss: 0.0028 - acc: 0.7261 - val_loss: 0.0024 - val_acc: 0.7103

Epoch 00013: val_loss did not improve
Epoch 14/80
1712/1712 [==============================] - 1s 448us/step - loss: 0.0025 - acc: 0.7144 - val_loss: 0.0019 - val_acc: 0.7150

Epoch 00014: val_loss improved from 0.00212 to 0.00188, saving model to my_model_RMSprop.h5
Epoch 15/80
1712/1712 [==============================] - 1s 445us/step - loss: 0.0024 - acc: 0.7167 - val_loss: 0.0022 - val_acc: 0.7079

Epoch 00015: val_loss did not improve
Epoch 16/80
1712/1712 [==============================] - 1s 448us/step - loss: 0.0022 - acc: 0.7261 - val_loss: 0.0022 - val_acc: 0.7079

Epoch 00016: val_loss did not improve
Epoch 17/80
1712/1712 [==============================] - 1s 449us/step - loss: 0.0021 - acc: 0.7407 - val_loss: 0.0017 - val_acc: 0.7243

Epoch 00017: val_loss improved from 0.00188 to 0.00173, saving model to my_model_RMSprop.h5
Epoch 18/80
1712/1712 [==============================] - 1s 436us/step - loss: 0.0020 - acc: 0.7436 - val_loss: 0.0024 - val_acc: 0.7243

Epoch 00018: val_loss did not improve
Epoch 19/80
1712/1712 [==============================] - 1s 439us/step - loss: 0.0019 - acc: 0.7383 - val_loss: 0.0016 - val_acc: 0.7313

Epoch 00019: val_loss improved from 0.00173 to 0.00158, saving model to my_model_RMSprop.h5
Epoch 20/80
1712/1712 [==============================] - 1s 441us/step - loss: 0.0018 - acc: 0.7576 - val_loss: 0.0025 - val_acc: 0.7150

Epoch 00020: val_loss did not improve
Epoch 21/80
1712/1712 [==============================] - 1s 451us/step - loss: 0.0017 - acc: 0.7617 - val_loss: 0.0016 - val_acc: 0.7640

Epoch 00021: val_loss improved from 0.00158 to 0.00155, saving model to my_model_RMSprop.h5
Epoch 22/80
1712/1712 [==============================] - 1s 432us/step - loss: 0.0016 - acc: 0.7675 - val_loss: 0.0014 - val_acc: 0.7617

Epoch 00022: val_loss improved from 0.00155 to 0.00137, saving model to my_model_RMSprop.h5
Epoch 23/80
1712/1712 [==============================] - 1s 446us/step - loss: 0.0015 - acc: 0.7699 - val_loss: 0.0013 - val_acc: 0.7687

Epoch 00023: val_loss improved from 0.00137 to 0.00133, saving model to my_model_RMSprop.h5
Epoch 24/80
1712/1712 [==============================] - 1s 471us/step - loss: 0.0014 - acc: 0.7716 - val_loss: 0.0014 - val_acc: 0.7593

Epoch 00024: val_loss did not improve
Epoch 25/80
1712/1712 [==============================] - 1s 451us/step - loss: 0.0014 - acc: 0.7821 - val_loss: 0.0016 - val_acc: 0.7664

Epoch 00025: val_loss did not improve
Epoch 26/80
1712/1712 [==============================] - 1s 455us/step - loss: 0.0014 - acc: 0.7745 - val_loss: 0.0013 - val_acc: 0.7547

Epoch 00026: val_loss did not improve
Epoch 27/80
1712/1712 [==============================] - 1s 436us/step - loss: 0.0012 - acc: 0.7868 - val_loss: 0.0015 - val_acc: 0.7710

Epoch 00027: val_loss did not improve
Epoch 28/80
1712/1712 [==============================] - 1s 445us/step - loss: 0.0012 - acc: 0.7821 - val_loss: 0.0013 - val_acc: 0.7710

Epoch 00028: val_loss improved from 0.00133 to 0.00126, saving model to my_model_RMSprop.h5
Epoch 29/80
1712/1712 [==============================] - 1s 443us/step - loss: 0.0011 - acc: 0.7874 - val_loss: 0.0014 - val_acc: 0.7804

Epoch 00029: val_loss did not improve
Epoch 30/80
1712/1712 [==============================] - 1s 465us/step - loss: 0.0011 - acc: 0.7979 - val_loss: 0.0014 - val_acc: 0.7593

Epoch 00030: val_loss did not improve
Epoch 31/80
1712/1712 [==============================] - 1s 453us/step - loss: 0.0011 - acc: 0.7897 - val_loss: 0.0012 - val_acc: 0.7710

Epoch 00031: val_loss improved from 0.00126 to 0.00119, saving model to my_model_RMSprop.h5
Epoch 32/80
1712/1712 [==============================] - 1s 456us/step - loss: 0.0010 - acc: 0.7991 - val_loss: 0.0013 - val_acc: 0.7664

Epoch 00032: val_loss did not improve
Epoch 33/80
1712/1712 [==============================] - 1s 462us/step - loss: 0.0010 - acc: 0.8061 - val_loss: 0.0012 - val_acc: 0.7850

Epoch 00033: val_loss did not improve
Epoch 34/80
1712/1712 [==============================] - 1s 426us/step - loss: 9.8931e-04 - acc: 0.8102 - val_loss: 0.0012 - val_acc: 0.7640

Epoch 00034: val_loss did not improve
Epoch 35/80
1712/1712 [==============================] - 1s 439us/step - loss: 9.7327e-04 - acc: 0.8207 - val_loss: 0.0012 - val_acc: 0.7804

Epoch 00035: val_loss did not improve
Epoch 36/80
1712/1712 [==============================] - 1s 454us/step - loss: 9.4467e-04 - acc: 0.8014 - val_loss: 0.0013 - val_acc: 0.7664

Epoch 00036: val_loss did not improve
Epoch 37/80
1712/1712 [==============================] - 1s 448us/step - loss: 9.0403e-04 - acc: 0.8037 - val_loss: 0.0012 - val_acc: 0.7710

Epoch 00037: val_loss improved from 0.00119 to 0.00118, saving model to my_model_RMSprop.h5
Epoch 38/80
1712/1712 [==============================] - 1s 455us/step - loss: 9.1130e-04 - acc: 0.8125 - val_loss: 0.0011 - val_acc: 0.7874

Epoch 00038: val_loss improved from 0.00118 to 0.00113, saving model to my_model_RMSprop.h5
Epoch 39/80
1712/1712 [==============================] - 1s 458us/step - loss: 8.8493e-04 - acc: 0.8096 - val_loss: 0.0012 - val_acc: 0.7710

Epoch 00039: val_loss did not improve
Epoch 40/80
1712/1712 [==============================] - 1s 440us/step - loss: 8.5503e-04 - acc: 0.8090 - val_loss: 0.0012 - val_acc: 0.7967

Epoch 00040: val_loss did not improve
Epoch 41/80
1712/1712 [==============================] - 1s 448us/step - loss: 8.5498e-04 - acc: 0.8172 - val_loss: 0.0011 - val_acc: 0.7757

Epoch 00041: val_loss did not improve
Epoch 42/80
1712/1712 [==============================] - 1s 453us/step - loss: 8.2676e-04 - acc: 0.8294 - val_loss: 0.0012 - val_acc: 0.7850

Epoch 00042: val_loss did not improve
Epoch 43/80
1712/1712 [==============================] - 1s 444us/step - loss: 8.4299e-04 - acc: 0.8201 - val_loss: 0.0011 - val_acc: 0.7874

Epoch 00043: val_loss did not improve
Epoch 44/80
1712/1712 [==============================] - 1s 438us/step - loss: 7.9454e-04 - acc: 0.8294 - val_loss: 0.0011 - val_acc: 0.8061

Epoch 00044: val_loss improved from 0.00113 to 0.00111, saving model to my_model_RMSprop.h5
Epoch 45/80
1712/1712 [==============================] - 1s 449us/step - loss: 7.9163e-04 - acc: 0.8271 - val_loss: 0.0011 - val_acc: 0.7827

Epoch 00045: val_loss did not improve
Epoch 46/80
1712/1712 [==============================] - 1s 461us/step - loss: 7.8349e-04 - acc: 0.8289 - val_loss: 0.0011 - val_acc: 0.7850

Epoch 00046: val_loss did not improve
Epoch 47/80
1712/1712 [==============================] - 1s 452us/step - loss: 8.0320e-04 - acc: 0.8154 - val_loss: 0.0011 - val_acc: 0.7734

Epoch 00047: val_loss did not improve
Epoch 48/80
1712/1712 [==============================] - 1s 445us/step - loss: 7.7107e-04 - acc: 0.8154 - val_loss: 0.0011 - val_acc: 0.7780

Epoch 00048: val_loss did not improve
Epoch 49/80
1712/1712 [==============================] - 1s 459us/step - loss: 7.8850e-04 - acc: 0.8318 - val_loss: 0.0011 - val_acc: 0.7944

Epoch 00049: val_loss improved from 0.00111 to 0.00111, saving model to my_model_RMSprop.h5
Epoch 50/80
1712/1712 [==============================] - 1s 448us/step - loss: 7.9337e-04 - acc: 0.8283 - val_loss: 0.0011 - val_acc: 0.7921

Epoch 00050: val_loss did not improve
Epoch 51/80
1712/1712 [==============================] - ETA: 0s - loss: 7.8257e-04 - acc: 0.820 - 1s 443us/step - loss: 7.7825e-04 - acc: 0.8213 - val_loss: 0.0012 - val_acc: 0.8131

Epoch 00051: val_loss did not improve
Epoch 52/80
1712/1712 [==============================] - 1s 447us/step - loss: 7.6615e-04 - acc: 0.8201 - val_loss: 0.0013 - val_acc: 0.7710

Epoch 00052: val_loss did not improve
Epoch 53/80
1712/1712 [==============================] - 1s 447us/step - loss: 7.4822e-04 - acc: 0.8131 - val_loss: 0.0011 - val_acc: 0.7967

Epoch 00053: val_loss did not improve
Epoch 54/80
1712/1712 [==============================] - 1s 455us/step - loss: 7.4716e-04 - acc: 0.8265 - val_loss: 0.0011 - val_acc: 0.7687

Epoch 00054: val_loss did not improve
Epoch 55/80
1712/1712 [==============================] - 1s 443us/step - loss: 7.2166e-04 - acc: 0.8440 - val_loss: 0.0011 - val_acc: 0.7780

Epoch 00055: val_loss improved from 0.00111 to 0.00111, saving model to my_model_RMSprop.h5
Epoch 56/80
1712/1712 [==============================] - 1s 451us/step - loss: 7.3858e-04 - acc: 0.8388 - val_loss: 0.0011 - val_acc: 0.7617

Epoch 00056: val_loss did not improve
Epoch 57/80
1712/1712 [==============================] - 1s 451us/step - loss: 7.0425e-04 - acc: 0.8318 - val_loss: 0.0012 - val_acc: 0.7734

Epoch 00057: val_loss did not improve
Epoch 58/80
1712/1712 [==============================] - 1s 452us/step - loss: 7.3822e-04 - acc: 0.8411 - val_loss: 0.0011 - val_acc: 0.7827

Epoch 00058: val_loss improved from 0.00111 to 0.00109, saving model to my_model_RMSprop.h5
Epoch 59/80
1712/1712 [==============================] - 1s 443us/step - loss: 7.1866e-04 - acc: 0.8364 - val_loss: 0.0012 - val_acc: 0.7850

Epoch 00059: val_loss did not improve
Epoch 60/80
1712/1712 [==============================] - 1s 441us/step - loss: 7.1380e-04 - acc: 0.8341 - val_loss: 0.0011 - val_acc: 0.7827

Epoch 00060: val_loss improved from 0.00109 to 0.00109, saving model to my_model_RMSprop.h5
Epoch 61/80
1712/1712 [==============================] - 1s 438us/step - loss: 6.9996e-04 - acc: 0.8440 - val_loss: 0.0012 - val_acc: 0.7921

Epoch 00061: val_loss did not improve
Epoch 62/80
1712/1712 [==============================] - 1s 432us/step - loss: 7.1379e-04 - acc: 0.8370 - val_loss: 0.0011 - val_acc: 0.7944

Epoch 00062: val_loss did not improve
Epoch 63/80
1712/1712 [==============================] - 1s 434us/step - loss: 7.3945e-04 - acc: 0.8423 - val_loss: 0.0011 - val_acc: 0.7921

Epoch 00063: val_loss did not improve
Epoch 64/80
1712/1712 [==============================] - 1s 448us/step - loss: 6.9853e-04 - acc: 0.8481 - val_loss: 0.0011 - val_acc: 0.7944

Epoch 00064: val_loss did not improve
Epoch 65/80
1712/1712 [==============================] - 1s 448us/step - loss: 7.1748e-04 - acc: 0.8493 - val_loss: 0.0011 - val_acc: 0.7804

Epoch 00065: val_loss did not improve
Epoch 66/80
1712/1712 [==============================] - 1s 441us/step - loss: 7.1832e-04 - acc: 0.8458 - val_loss: 0.0012 - val_acc: 0.7804

Epoch 00066: val_loss did not improve
Epoch 67/80
1712/1712 [==============================] - 1s 449us/step - loss: 6.8407e-04 - acc: 0.8516 - val_loss: 0.0011 - val_acc: 0.7944

Epoch 00067: val_loss did not improve
Epoch 68/80
1712/1712 [==============================] - 1s 442us/step - loss: 6.6932e-04 - acc: 0.8575 - val_loss: 0.0012 - val_acc: 0.7874

Epoch 00068: val_loss did not improve
Epoch 69/80
1712/1712 [==============================] - 1s 459us/step - loss: 6.9349e-04 - acc: 0.8481 - val_loss: 0.0011 - val_acc: 0.8014

Epoch 00069: val_loss did not improve
Epoch 70/80
1712/1712 [==============================] - 1s 446us/step - loss: 6.9624e-04 - acc: 0.8534 - val_loss: 0.0011 - val_acc: 0.8037

Epoch 00070: val_loss did not improve
Epoch 71/80
1712/1712 [==============================] - 1s 441us/step - loss: 6.5384e-04 - acc: 0.8487 - val_loss: 0.0011 - val_acc: 0.7944

Epoch 00071: val_loss did not improve
Epoch 72/80
1712/1712 [==============================] - 1s 452us/step - loss: 6.8570e-04 - acc: 0.8627 - val_loss: 0.0011 - val_acc: 0.7967

Epoch 00072: val_loss did not improve
Epoch 73/80
1712/1712 [==============================] - 1s 451us/step - loss: 6.9590e-04 - acc: 0.8458 - val_loss: 0.0011 - val_acc: 0.8178

Epoch 00073: val_loss improved from 0.00109 to 0.00109, saving model to my_model_RMSprop.h5
Epoch 74/80
1712/1712 [==============================] - 1s 437us/step - loss: 6.7575e-04 - acc: 0.8569 - val_loss: 0.0012 - val_acc: 0.7804

Epoch 00074: val_loss did not improve
Epoch 75/80
1712/1712 [==============================] - 1s 448us/step - loss: 6.5800e-04 - acc: 0.8569 - val_loss: 0.0011 - val_acc: 0.7991

Epoch 00075: val_loss did not improve
Epoch 76/80
1712/1712 [==============================] - 1s 444us/step - loss: 7.0128e-04 - acc: 0.8458 - val_loss: 0.0011 - val_acc: 0.7827

Epoch 00076: val_loss did not improve
Epoch 77/80
1712/1712 [==============================] - 1s 453us/step - loss: 7.0785e-04 - acc: 0.8487 - val_loss: 0.0012 - val_acc: 0.8014

Epoch 00077: val_loss did not improve
Epoch 78/80
1712/1712 [==============================] - 1s 441us/step - loss: 6.7042e-04 - acc: 0.8411 - val_loss: 0.0011 - val_acc: 0.8107

Epoch 00078: val_loss did not improve
Epoch 79/80
1712/1712 [==============================] - 1s 440us/step - loss: 6.6017e-04 - acc: 0.8435 - val_loss: 0.0011 - val_acc: 0.7944

Epoch 00079: val_loss improved from 0.00109 to 0.00108, saving model to my_model_RMSprop.h5
Epoch 80/80
1712/1712 [==============================] - 1s 460us/step - loss: 6.7387e-04 - acc: 0.8569 - val_loss: 0.0011 - val_acc: 0.8014

Epoch 00080: val_loss did not improve
------------------->Evaluating Adagrad <----------------------------------------
-----------------------------------------------------------------------------
Train on 1712 samples, validate on 428 samples
Epoch 1/80
1712/1712 [==============================] - 2s 1ms/step - loss: 0.1677 - acc: 0.3481 - val_loss: 0.0065 - val_acc: 0.6963

Epoch 00001: val_loss improved from inf to 0.00650, saving model to my_model_Adagrad.h5
Epoch 2/80
1712/1712 [==============================] - 1s 444us/step - loss: 0.0121 - acc: 0.5421 - val_loss: 0.0044 - val_acc: 0.6963

Epoch 00002: val_loss improved from 0.00650 to 0.00442, saving model to my_model_Adagrad.h5
Epoch 3/80
1712/1712 [==============================] - 1s 441us/step - loss: 0.0098 - acc: 0.5754 - val_loss: 0.0040 - val_acc: 0.6916

Epoch 00003: val_loss improved from 0.00442 to 0.00403, saving model to my_model_Adagrad.h5
Epoch 4/80
1712/1712 [==============================] - 1s 424us/step - loss: 0.0090 - acc: 0.5777 - val_loss: 0.0043 - val_acc: 0.6916

Epoch 00004: val_loss did not improve
Epoch 5/80
1712/1712 [==============================] - 1s 438us/step - loss: 0.0081 - acc: 0.6110 - val_loss: 0.0033 - val_acc: 0.6893

Epoch 00005: val_loss improved from 0.00403 to 0.00330, saving model to my_model_Adagrad.h5
Epoch 6/80
1712/1712 [==============================] - 1s 430us/step - loss: 0.0078 - acc: 0.6046 - val_loss: 0.0032 - val_acc: 0.6963

Epoch 00006: val_loss improved from 0.00330 to 0.00318, saving model to my_model_Adagrad.h5
Epoch 7/80
1712/1712 [==============================] - 1s 440us/step - loss: 0.0070 - acc: 0.6303 - val_loss: 0.0039 - val_acc: 0.6963

Epoch 00007: val_loss did not improve
Epoch 8/80
1712/1712 [==============================] - 1s 435us/step - loss: 0.0068 - acc: 0.6162 - val_loss: 0.0043 - val_acc: 0.7009

Epoch 00008: val_loss did not improve
Epoch 9/80
1712/1712 [==============================] - 1s 441us/step - loss: 0.0066 - acc: 0.6268 - val_loss: 0.0030 - val_acc: 0.6986

Epoch 00009: val_loss improved from 0.00318 to 0.00297, saving model to my_model_Adagrad.h5
Epoch 10/80
1712/1712 [==============================] - 1s 441us/step - loss: 0.0065 - acc: 0.6379 - val_loss: 0.0030 - val_acc: 0.7009

Epoch 00010: val_loss did not improve
Epoch 11/80
1712/1712 [==============================] - 1s 450us/step - loss: 0.0064 - acc: 0.6478 - val_loss: 0.0027 - val_acc: 0.6963

Epoch 00011: val_loss improved from 0.00297 to 0.00275, saving model to my_model_Adagrad.h5
Epoch 12/80
1712/1712 [==============================] - 1s 429us/step - loss: 0.0061 - acc: 0.6320 - val_loss: 0.0027 - val_acc: 0.6916

Epoch 00012: val_loss improved from 0.00275 to 0.00271, saving model to my_model_Adagrad.h5
Epoch 13/80
1712/1712 [==============================] - 1s 445us/step - loss: 0.0057 - acc: 0.6320 - val_loss: 0.0027 - val_acc: 0.6986

Epoch 00013: val_loss improved from 0.00271 to 0.00265, saving model to my_model_Adagrad.h5
Epoch 14/80
1712/1712 [==============================] - 1s 439us/step - loss: 0.0057 - acc: 0.6425 - val_loss: 0.0027 - val_acc: 0.7079

Epoch 00014: val_loss did not improve
Epoch 15/80
1712/1712 [==============================] - 1s 439us/step - loss: 0.0054 - acc: 0.6431 - val_loss: 0.0026 - val_acc: 0.7056

Epoch 00015: val_loss improved from 0.00265 to 0.00257, saving model to my_model_Adagrad.h5
Epoch 16/80
1712/1712 [==============================] - 1s 448us/step - loss: 0.0053 - acc: 0.6431 - val_loss: 0.0027 - val_acc: 0.6963

Epoch 00016: val_loss did not improve
Epoch 17/80
1712/1712 [==============================] - 1s 444us/step - loss: 0.0052 - acc: 0.6379 - val_loss: 0.0028 - val_acc: 0.7009

Epoch 00017: val_loss did not improve
Epoch 18/80
1712/1712 [==============================] - 1s 437us/step - loss: 0.0053 - acc: 0.6565 - val_loss: 0.0025 - val_acc: 0.6986

Epoch 00018: val_loss improved from 0.00257 to 0.00249, saving model to my_model_Adagrad.h5
Epoch 19/80
1712/1712 [==============================] - 1s 451us/step - loss: 0.0052 - acc: 0.6507 - val_loss: 0.0025 - val_acc: 0.7056

Epoch 00019: val_loss improved from 0.00249 to 0.00248, saving model to my_model_Adagrad.h5
Epoch 20/80
1712/1712 [==============================] - 1s 429us/step - loss: 0.0049 - acc: 0.6589 - val_loss: 0.0025 - val_acc: 0.7056

Epoch 00020: val_loss did not improve
Epoch 21/80
1712/1712 [==============================] - 1s 442us/step - loss: 0.0048 - acc: 0.6542 - val_loss: 0.0027 - val_acc: 0.7103

Epoch 00021: val_loss did not improve
Epoch 22/80
1712/1712 [==============================] - 1s 438us/step - loss: 0.0048 - acc: 0.6513 - val_loss: 0.0025 - val_acc: 0.7056

Epoch 00022: val_loss did not improve
Epoch 23/80
1712/1712 [==============================] - 1s 444us/step - loss: 0.0047 - acc: 0.6495 - val_loss: 0.0026 - val_acc: 0.7056

Epoch 00023: val_loss did not improve
Epoch 24/80
1712/1712 [==============================] - 1s 426us/step - loss: 0.0047 - acc: 0.6460 - val_loss: 0.0023 - val_acc: 0.7103

Epoch 00024: val_loss improved from 0.00248 to 0.00233, saving model to my_model_Adagrad.h5
Epoch 25/80
1712/1712 [==============================] - 1s 447us/step - loss: 0.0046 - acc: 0.6723 - val_loss: 0.0023 - val_acc: 0.7056

Epoch 00025: val_loss improved from 0.00233 to 0.00230, saving model to my_model_Adagrad.h5
Epoch 26/80
1712/1712 [==============================] - 1s 438us/step - loss: 0.0044 - acc: 0.6659 - val_loss: 0.0024 - val_acc: 0.7033

Epoch 00026: val_loss did not improve
Epoch 27/80
1712/1712 [==============================] - 1s 440us/step - loss: 0.0043 - acc: 0.6863 - val_loss: 0.0023 - val_acc: 0.7033

Epoch 00027: val_loss did not improve
Epoch 28/80
1712/1712 [==============================] - 1s 434us/step - loss: 0.0042 - acc: 0.6857 - val_loss: 0.0023 - val_acc: 0.7079

Epoch 00028: val_loss improved from 0.00230 to 0.00227, saving model to my_model_Adagrad.h5
Epoch 29/80
1712/1712 [==============================] - 1s 434us/step - loss: 0.0043 - acc: 0.6676 - val_loss: 0.0024 - val_acc: 0.7033

Epoch 00029: val_loss did not improve
Epoch 30/80
1712/1712 [==============================] - 1s 435us/step - loss: 0.0043 - acc: 0.6671 - val_loss: 0.0024 - val_acc: 0.7033

Epoch 00030: val_loss did not improve
Epoch 31/80
1712/1712 [==============================] - 1s 437us/step - loss: 0.0044 - acc: 0.6741 - val_loss: 0.0024 - val_acc: 0.7079

Epoch 00031: val_loss did not improve
Epoch 32/80
1712/1712 [==============================] - 1s 437us/step - loss: 0.0041 - acc: 0.6863 - val_loss: 0.0023 - val_acc: 0.7103

Epoch 00032: val_loss did not improve
Epoch 33/80
1712/1712 [==============================] - 1s 429us/step - loss: 0.0040 - acc: 0.6916 - val_loss: 0.0022 - val_acc: 0.7150

Epoch 00033: val_loss improved from 0.00227 to 0.00216, saving model to my_model_Adagrad.h5
Epoch 34/80
1712/1712 [==============================] - 1s 435us/step - loss: 0.0040 - acc: 0.6729 - val_loss: 0.0027 - val_acc: 0.7150

Epoch 00034: val_loss did not improve
Epoch 35/80
1712/1712 [==============================] - 1s 441us/step - loss: 0.0040 - acc: 0.6746 - val_loss: 0.0024 - val_acc: 0.7079

Epoch 00035: val_loss did not improve
Epoch 36/80
1712/1712 [==============================] - 1s 442us/step - loss: 0.0040 - acc: 0.6717 - val_loss: 0.0026 - val_acc: 0.7103

Epoch 00036: val_loss did not improve
Epoch 37/80
1712/1712 [==============================] - 1s 449us/step - loss: 0.0039 - acc: 0.6928 - val_loss: 0.0024 - val_acc: 0.7196

Epoch 00037: val_loss did not improve
Epoch 38/80
1712/1712 [==============================] - 1s 444us/step - loss: 0.0039 - acc: 0.6916 - val_loss: 0.0026 - val_acc: 0.7173

Epoch 00038: val_loss did not improve
Epoch 39/80
1712/1712 [==============================] - 1s 428us/step - loss: 0.0038 - acc: 0.6904 - val_loss: 0.0021 - val_acc: 0.7173

Epoch 00039: val_loss improved from 0.00216 to 0.00210, saving model to my_model_Adagrad.h5
Epoch 40/80
1712/1712 [==============================] - 1s 450us/step - loss: 0.0037 - acc: 0.6945 - val_loss: 0.0021 - val_acc: 0.7103

Epoch 00040: val_loss did not improve
Epoch 41/80
1712/1712 [==============================] - 1s 438us/step - loss: 0.0037 - acc: 0.6939 - val_loss: 0.0021 - val_acc: 0.7220

Epoch 00041: val_loss improved from 0.00210 to 0.00205, saving model to my_model_Adagrad.h5
Epoch 42/80
1712/1712 [==============================] - 1s 466us/step - loss: 0.0036 - acc: 0.6887 - val_loss: 0.0022 - val_acc: 0.7126

Epoch 00042: val_loss did not improve
Epoch 43/80
1712/1712 [==============================] - 1s 445us/step - loss: 0.0037 - acc: 0.6881 - val_loss: 0.0021 - val_acc: 0.7196

Epoch 00043: val_loss did not improve
Epoch 44/80
1712/1712 [==============================] - 1s 432us/step - loss: 0.0037 - acc: 0.6963 - val_loss: 0.0021 - val_acc: 0.7220

Epoch 00044: val_loss did not improve
Epoch 45/80
1712/1712 [==============================] - 1s 437us/step - loss: 0.0036 - acc: 0.7021 - val_loss: 0.0020 - val_acc: 0.7079

Epoch 00045: val_loss improved from 0.00205 to 0.00205, saving model to my_model_Adagrad.h5
Epoch 46/80
1712/1712 [==============================] - 1s 450us/step - loss: 0.0035 - acc: 0.6957 - val_loss: 0.0020 - val_acc: 0.7126

Epoch 00046: val_loss improved from 0.00205 to 0.00203, saving model to my_model_Adagrad.h5
Epoch 47/80
1712/1712 [==============================] - 1s 454us/step - loss: 0.0036 - acc: 0.6939 - val_loss: 0.0020 - val_acc: 0.7243

Epoch 00047: val_loss improved from 0.00203 to 0.00203, saving model to my_model_Adagrad.h5
Epoch 48/80
1712/1712 [==============================] - 1s 441us/step - loss: 0.0035 - acc: 0.6963 - val_loss: 0.0021 - val_acc: 0.7103

Epoch 00048: val_loss did not improve
Epoch 49/80
1712/1712 [==============================] - 1s 432us/step - loss: 0.0034 - acc: 0.6910 - val_loss: 0.0022 - val_acc: 0.7220

Epoch 00049: val_loss did not improve
Epoch 50/80
1712/1712 [==============================] - 1s 430us/step - loss: 0.0035 - acc: 0.6986 - val_loss: 0.0020 - val_acc: 0.7243

Epoch 00050: val_loss improved from 0.00203 to 0.00199, saving model to my_model_Adagrad.h5
Epoch 51/80
1712/1712 [==============================] - ETA: 0s - loss: 0.0034 - acc: 0.698 - 1s 442us/step - loss: 0.0034 - acc: 0.6963 - val_loss: 0.0020 - val_acc: 0.7196

Epoch 00051: val_loss did not improve
Epoch 52/80
1712/1712 [==============================] - 1s 434us/step - loss: 0.0033 - acc: 0.6957 - val_loss: 0.0020 - val_acc: 0.7103

Epoch 00052: val_loss improved from 0.00199 to 0.00196, saving model to my_model_Adagrad.h5
Epoch 53/80
1712/1712 [==============================] - 1s 429us/step - loss: 0.0033 - acc: 0.7004 - val_loss: 0.0020 - val_acc: 0.7079

Epoch 00053: val_loss did not improve
Epoch 54/80
1712/1712 [==============================] - 1s 444us/step - loss: 0.0033 - acc: 0.7062 - val_loss: 0.0020 - val_acc: 0.7103

Epoch 00054: val_loss did not improve
Epoch 55/80
1712/1712 [==============================] - 1s 430us/step - loss: 0.0033 - acc: 0.6980 - val_loss: 0.0023 - val_acc: 0.7126

Epoch 00055: val_loss did not improve
Epoch 56/80
1712/1712 [==============================] - 1s 439us/step - loss: 0.0032 - acc: 0.7050 - val_loss: 0.0020 - val_acc: 0.7173

Epoch 00056: val_loss did not improve
Epoch 57/80
1712/1712 [==============================] - 1s 435us/step - loss: 0.0033 - acc: 0.7021 - val_loss: 0.0019 - val_acc: 0.7150

Epoch 00057: val_loss improved from 0.00196 to 0.00194, saving model to my_model_Adagrad.h5
Epoch 58/80
1712/1712 [==============================] - 1s 438us/step - loss: 0.0032 - acc: 0.6992 - val_loss: 0.0021 - val_acc: 0.7103

Epoch 00058: val_loss did not improve
Epoch 59/80
1712/1712 [==============================] - 1s 443us/step - loss: 0.0032 - acc: 0.7062 - val_loss: 0.0023 - val_acc: 0.7103

Epoch 00059: val_loss did not improve
Epoch 60/80
1712/1712 [==============================] - 1s 441us/step - loss: 0.0031 - acc: 0.7027 - val_loss: 0.0019 - val_acc: 0.7150

Epoch 00060: val_loss improved from 0.00194 to 0.00191, saving model to my_model_Adagrad.h5
Epoch 61/80
1712/1712 [==============================] - 1s 443us/step - loss: 0.0031 - acc: 0.7079 - val_loss: 0.0020 - val_acc: 0.7150

Epoch 00061: val_loss did not improve
Epoch 62/80
1712/1712 [==============================] - 1s 440us/step - loss: 0.0031 - acc: 0.7150 - val_loss: 0.0019 - val_acc: 0.7196

Epoch 00062: val_loss did not improve
Epoch 63/80
1712/1712 [==============================] - 1s 436us/step - loss: 0.0032 - acc: 0.6968 - val_loss: 0.0019 - val_acc: 0.7196

Epoch 00063: val_loss improved from 0.00191 to 0.00190, saving model to my_model_Adagrad.h5
Epoch 64/80
1712/1712 [==============================] - 1s 440us/step - loss: 0.0031 - acc: 0.7132 - val_loss: 0.0020 - val_acc: 0.7220

Epoch 00064: val_loss did not improve
Epoch 65/80
1712/1712 [==============================] - 1s 440us/step - loss: 0.0031 - acc: 0.7068 - val_loss: 0.0019 - val_acc: 0.7220

Epoch 00065: val_loss did not improve
Epoch 66/80
1712/1712 [==============================] - 1s 449us/step - loss: 0.0031 - acc: 0.7138 - val_loss: 0.0019 - val_acc: 0.7196

Epoch 00066: val_loss improved from 0.00190 to 0.00187, saving model to my_model_Adagrad.h5
Epoch 67/80
1712/1712 [==============================] - 1s 443us/step - loss: 0.0030 - acc: 0.7144 - val_loss: 0.0020 - val_acc: 0.7266

Epoch 00067: val_loss did not improve
Epoch 68/80
1712/1712 [==============================] - 1s 449us/step - loss: 0.0029 - acc: 0.7033 - val_loss: 0.0020 - val_acc: 0.7196

Epoch 00068: val_loss did not improve
Epoch 69/80
1712/1712 [==============================] - 1s 453us/step - loss: 0.0030 - acc: 0.7120 - val_loss: 0.0020 - val_acc: 0.7173

Epoch 00069: val_loss did not improve
Epoch 70/80
1712/1712 [==============================] - 1s 436us/step - loss: 0.0030 - acc: 0.7097 - val_loss: 0.0019 - val_acc: 0.7196

Epoch 00070: val_loss did not improve
Epoch 71/80
1712/1712 [==============================] - 1s 431us/step - loss: 0.0030 - acc: 0.7114 - val_loss: 0.0019 - val_acc: 0.7220

Epoch 00071: val_loss did not improve
Epoch 72/80
1712/1712 [==============================] - 1s 436us/step - loss: 0.0030 - acc: 0.7167 - val_loss: 0.0018 - val_acc: 0.7220

Epoch 00072: val_loss improved from 0.00187 to 0.00183, saving model to my_model_Adagrad.h5
Epoch 73/80
1712/1712 [==============================] - 1s 439us/step - loss: 0.0031 - acc: 0.7056 - val_loss: 0.0020 - val_acc: 0.7196

Epoch 00073: val_loss did not improve
Epoch 74/80
1712/1712 [==============================] - 1s 430us/step - loss: 0.0028 - acc: 0.7126 - val_loss: 0.0020 - val_acc: 0.7266

Epoch 00074: val_loss did not improve
Epoch 75/80
1712/1712 [==============================] - 1s 445us/step - loss: 0.0029 - acc: 0.7155 - val_loss: 0.0019 - val_acc: 0.7196

Epoch 00075: val_loss did not improve
Epoch 76/80
1712/1712 [==============================] - 1s 444us/step - loss: 0.0029 - acc: 0.7243 - val_loss: 0.0018 - val_acc: 0.7243

Epoch 00076: val_loss did not improve
Epoch 77/80
1712/1712 [==============================] - 1s 448us/step - loss: 0.0028 - acc: 0.7266 - val_loss: 0.0019 - val_acc: 0.7243

Epoch 00077: val_loss did not improve
Epoch 78/80
1712/1712 [==============================] - 1s 439us/step - loss: 0.0029 - acc: 0.7208 - val_loss: 0.0018 - val_acc: 0.7220

Epoch 00078: val_loss improved from 0.00183 to 0.00183, saving model to my_model_Adagrad.h5
Epoch 79/80
1712/1712 [==============================] - 1s 436us/step - loss: 0.0029 - acc: 0.7126 - val_loss: 0.0019 - val_acc: 0.7266

Epoch 00079: val_loss did not improve
Epoch 80/80
1712/1712 [==============================] - 1s 436us/step - loss: 0.0028 - acc: 0.7249 - val_loss: 0.0018 - val_acc: 0.7266

Epoch 00080: val_loss improved from 0.00183 to 0.00180, saving model to my_model_Adagrad.h5
------------------->Evaluating Adadelta <----------------------------------------
-----------------------------------------------------------------------------
Train on 1712 samples, validate on 428 samples
Epoch 1/80
1712/1712 [==============================] - 2s 1ms/step - loss: 0.0302 - acc: 0.3902 - val_loss: 0.0088 - val_acc: 0.6963

Epoch 00001: val_loss improved from inf to 0.00880, saving model to my_model_Adadelta.h5
Epoch 2/80
1712/1712 [==============================] - 1s 469us/step - loss: 0.0117 - acc: 0.5386 - val_loss: 0.0046 - val_acc: 0.6963

Epoch 00002: val_loss improved from 0.00880 to 0.00464, saving model to my_model_Adadelta.h5
Epoch 3/80
1712/1712 [==============================] - 1s 471us/step - loss: 0.0087 - acc: 0.5970 - val_loss: 0.0044 - val_acc: 0.6963

Epoch 00003: val_loss improved from 0.00464 to 0.00438, saving model to my_model_Adadelta.h5
Epoch 4/80
1712/1712 [==============================] - 1s 483us/step - loss: 0.0074 - acc: 0.6192 - val_loss: 0.0043 - val_acc: 0.6963

Epoch 00004: val_loss improved from 0.00438 to 0.00431, saving model to my_model_Adadelta.h5
Epoch 5/80
1712/1712 [==============================] - 1s 473us/step - loss: 0.0066 - acc: 0.6238 - val_loss: 0.0047 - val_acc: 0.6963

Epoch 00005: val_loss did not improve
Epoch 6/80
1712/1712 [==============================] - ETA: 0s - loss: 0.0062 - acc: 0.633 - 1s 472us/step - loss: 0.0061 - acc: 0.6314 - val_loss: 0.0042 - val_acc: 0.6963

Epoch 00006: val_loss improved from 0.00431 to 0.00415, saving model to my_model_Adadelta.h5
Epoch 7/80
1712/1712 [==============================] - 1s 456us/step - loss: 0.0057 - acc: 0.6711 - val_loss: 0.0041 - val_acc: 0.6963

Epoch 00007: val_loss improved from 0.00415 to 0.00406, saving model to my_model_Adadelta.h5
Epoch 8/80
1712/1712 [==============================] - 1s 465us/step - loss: 0.0054 - acc: 0.6671 - val_loss: 0.0045 - val_acc: 0.6963

Epoch 00008: val_loss did not improve
Epoch 9/80
1712/1712 [==============================] - 1s 472us/step - loss: 0.0052 - acc: 0.6805 - val_loss: 0.0038 - val_acc: 0.6963

Epoch 00009: val_loss improved from 0.00406 to 0.00384, saving model to my_model_Adadelta.h5
Epoch 10/80
1712/1712 [==============================] - 1s 483us/step - loss: 0.0049 - acc: 0.6881 - val_loss: 0.0043 - val_acc: 0.6963

Epoch 00010: val_loss did not improve
Epoch 11/80
1712/1712 [==============================] - 1s 473us/step - loss: 0.0047 - acc: 0.6893 - val_loss: 0.0040 - val_acc: 0.7033

Epoch 00011: val_loss did not improve
Epoch 12/80
1712/1712 [==============================] - 1s 460us/step - loss: 0.0044 - acc: 0.7021 - val_loss: 0.0039 - val_acc: 0.7033

Epoch 00012: val_loss did not improve
Epoch 13/80
1712/1712 [==============================] - 1s 489us/step - loss: 0.0044 - acc: 0.7004 - val_loss: 0.0037 - val_acc: 0.7009

Epoch 00013: val_loss improved from 0.00384 to 0.00369, saving model to my_model_Adadelta.h5
Epoch 14/80
1712/1712 [==============================] - 1s 466us/step - loss: 0.0041 - acc: 0.7033 - val_loss: 0.0039 - val_acc: 0.7009

Epoch 00014: val_loss did not improve
Epoch 15/80
1712/1712 [==============================] - 1s 474us/step - loss: 0.0039 - acc: 0.7068 - val_loss: 0.0047 - val_acc: 0.7056

Epoch 00015: val_loss did not improve
Epoch 16/80
1712/1712 [==============================] - 1s 464us/step - loss: 0.0038 - acc: 0.7132 - val_loss: 0.0033 - val_acc: 0.7079

Epoch 00016: val_loss improved from 0.00369 to 0.00329, saving model to my_model_Adadelta.h5
Epoch 17/80
1712/1712 [==============================] - 1s 489us/step - loss: 0.0036 - acc: 0.7342 - val_loss: 0.0036 - val_acc: 0.7033

Epoch 00017: val_loss did not improve
Epoch 18/80
1712/1712 [==============================] - 1s 474us/step - loss: 0.0035 - acc: 0.7126 - val_loss: 0.0031 - val_acc: 0.7220

Epoch 00018: val_loss improved from 0.00329 to 0.00311, saving model to my_model_Adadelta.h5
Epoch 19/80
1712/1712 [==============================] - 1s 468us/step - loss: 0.0034 - acc: 0.7196 - val_loss: 0.0033 - val_acc: 0.7220

Epoch 00019: val_loss did not improve
Epoch 20/80
1712/1712 [==============================] - 1s 470us/step - loss: 0.0032 - acc: 0.7342 - val_loss: 0.0029 - val_acc: 0.7243

Epoch 00020: val_loss improved from 0.00311 to 0.00290, saving model to my_model_Adadelta.h5
Epoch 21/80
1712/1712 [==============================] - 1s 482us/step - loss: 0.0030 - acc: 0.7208 - val_loss: 0.0029 - val_acc: 0.7290

Epoch 00021: val_loss did not improve
Epoch 22/80
1712/1712 [==============================] - 1s 471us/step - loss: 0.0029 - acc: 0.7313 - val_loss: 0.0029 - val_acc: 0.7220

Epoch 00022: val_loss improved from 0.00290 to 0.00289, saving model to my_model_Adadelta.h5
Epoch 23/80
1712/1712 [==============================] - 1s 473us/step - loss: 0.0028 - acc: 0.7342 - val_loss: 0.0027 - val_acc: 0.7290

Epoch 00023: val_loss improved from 0.00289 to 0.00269, saving model to my_model_Adadelta.h5
Epoch 24/80
1712/1712 [==============================] - 1s 469us/step - loss: 0.0027 - acc: 0.7342 - val_loss: 0.0025 - val_acc: 0.7266

Epoch 00024: val_loss improved from 0.00269 to 0.00246, saving model to my_model_Adadelta.h5
Epoch 25/80
1712/1712 [==============================] - 1s 484us/step - loss: 0.0026 - acc: 0.7319 - val_loss: 0.0023 - val_acc: 0.7360

Epoch 00025: val_loss improved from 0.00246 to 0.00229, saving model to my_model_Adadelta.h5
Epoch 26/80
1712/1712 [==============================] - 1s 475us/step - loss: 0.0025 - acc: 0.7336 - val_loss: 0.0023 - val_acc: 0.7407

Epoch 00026: val_loss improved from 0.00229 to 0.00226, saving model to my_model_Adadelta.h5
Epoch 27/80
1712/1712 [==============================] - 1s 469us/step - loss: 0.0024 - acc: 0.7430 - val_loss: 0.0022 - val_acc: 0.7383

Epoch 00027: val_loss improved from 0.00226 to 0.00221, saving model to my_model_Adadelta.h5
Epoch 28/80
1712/1712 [==============================] - 1s 479us/step - loss: 0.0023 - acc: 0.7453 - val_loss: 0.0022 - val_acc: 0.7336

Epoch 00028: val_loss improved from 0.00221 to 0.00217, saving model to my_model_Adadelta.h5
Epoch 29/80
1712/1712 [==============================] - 1s 470us/step - loss: 0.0023 - acc: 0.7342 - val_loss: 0.0022 - val_acc: 0.7290

Epoch 00029: val_loss did not improve
Epoch 30/80
1712/1712 [==============================] - 1s 478us/step - loss: 0.0022 - acc: 0.7407 - val_loss: 0.0020 - val_acc: 0.7336

Epoch 00030: val_loss improved from 0.00217 to 0.00205, saving model to my_model_Adadelta.h5
Epoch 31/80
1712/1712 [==============================] - 1s 480us/step - loss: 0.0022 - acc: 0.7477 - val_loss: 0.0020 - val_acc: 0.7243

Epoch 00031: val_loss improved from 0.00205 to 0.00199, saving model to my_model_Adadelta.h5
Epoch 32/80
1712/1712 [==============================] - 1s 471us/step - loss: 0.0021 - acc: 0.7482 - val_loss: 0.0020 - val_acc: 0.7360

Epoch 00032: val_loss did not improve
Epoch 33/80
1712/1712 [==============================] - 1s 463us/step - loss: 0.0021 - acc: 0.7442 - val_loss: 0.0019 - val_acc: 0.7477

Epoch 00033: val_loss improved from 0.00199 to 0.00194, saving model to my_model_Adadelta.h5
Epoch 34/80
1712/1712 [==============================] - 1s 472us/step - loss: 0.0020 - acc: 0.7471 - val_loss: 0.0019 - val_acc: 0.7336

Epoch 00034: val_loss improved from 0.00194 to 0.00189, saving model to my_model_Adadelta.h5
Epoch 35/80
1712/1712 [==============================] - 1s 477us/step - loss: 0.0020 - acc: 0.7319 - val_loss: 0.0019 - val_acc: 0.7430

Epoch 00035: val_loss did not improve
Epoch 36/80
1712/1712 [==============================] - 1s 475us/step - loss: 0.0020 - acc: 0.7512 - val_loss: 0.0019 - val_acc: 0.7383

Epoch 00036: val_loss improved from 0.00189 to 0.00185, saving model to my_model_Adadelta.h5
Epoch 37/80
1712/1712 [==============================] - 1s 468us/step - loss: 0.0019 - acc: 0.7477 - val_loss: 0.0022 - val_acc: 0.7453

Epoch 00037: val_loss did not improve
Epoch 38/80
1712/1712 [==============================] - 1s 476us/step - loss: 0.0019 - acc: 0.7582 - val_loss: 0.0018 - val_acc: 0.7383

Epoch 00038: val_loss improved from 0.00185 to 0.00178, saving model to my_model_Adadelta.h5
Epoch 39/80
1712/1712 [==============================] - 1s 468us/step - loss: 0.0019 - acc: 0.7523 - val_loss: 0.0019 - val_acc: 0.7430

Epoch 00039: val_loss did not improve
Epoch 40/80
1712/1712 [==============================] - 1s 473us/step - loss: 0.0018 - acc: 0.7482 - val_loss: 0.0019 - val_acc: 0.7500

Epoch 00040: val_loss did not improve
Epoch 41/80
1712/1712 [==============================] - 1s 471us/step - loss: 0.0018 - acc: 0.7588 - val_loss: 0.0017 - val_acc: 0.7313

Epoch 00041: val_loss improved from 0.00178 to 0.00174, saving model to my_model_Adadelta.h5
Epoch 42/80
1712/1712 [==============================] - 1s 471us/step - loss: 0.0018 - acc: 0.7477 - val_loss: 0.0017 - val_acc: 0.7407

Epoch 00042: val_loss improved from 0.00174 to 0.00172, saving model to my_model_Adadelta.h5
Epoch 43/80
1712/1712 [==============================] - 1s 474us/step - loss: 0.0017 - acc: 0.7582 - val_loss: 0.0017 - val_acc: 0.7407

Epoch 00043: val_loss improved from 0.00172 to 0.00172, saving model to my_model_Adadelta.h5
Epoch 44/80
1712/1712 [==============================] - 1s 470us/step - loss: 0.0017 - acc: 0.7558 - val_loss: 0.0017 - val_acc: 0.7430

Epoch 00044: val_loss improved from 0.00172 to 0.00168, saving model to my_model_Adadelta.h5
Epoch 45/80
1712/1712 [==============================] - 1s 466us/step - loss: 0.0017 - acc: 0.7523 - val_loss: 0.0019 - val_acc: 0.7477

Epoch 00045: val_loss did not improve
Epoch 46/80
1712/1712 [==============================] - 1s 467us/step - loss: 0.0017 - acc: 0.7588 - val_loss: 0.0016 - val_acc: 0.7430

Epoch 00046: val_loss improved from 0.00168 to 0.00164, saving model to my_model_Adadelta.h5
Epoch 47/80
1712/1712 [==============================] - 1s 470us/step - loss: 0.0017 - acc: 0.7599 - val_loss: 0.0018 - val_acc: 0.7407

Epoch 00047: val_loss did not improve
Epoch 48/80
1712/1712 [==============================] - 1s 470us/step - loss: 0.0016 - acc: 0.7518 - val_loss: 0.0016 - val_acc: 0.7430

Epoch 00048: val_loss did not improve
Epoch 49/80
1712/1712 [==============================] - 1s 474us/step - loss: 0.0016 - acc: 0.7634 - val_loss: 0.0016 - val_acc: 0.7453

Epoch 00049: val_loss improved from 0.00164 to 0.00161, saving model to my_model_Adadelta.h5
Epoch 50/80
1712/1712 [==============================] - 1s 467us/step - loss: 0.0016 - acc: 0.7646 - val_loss: 0.0017 - val_acc: 0.7360

Epoch 00050: val_loss did not improve
Epoch 51/80
1712/1712 [==============================] - 1s 475us/step - loss: 0.0016 - acc: 0.7582 - val_loss: 0.0016 - val_acc: 0.7453

Epoch 00051: val_loss improved from 0.00161 to 0.00160, saving model to my_model_Adadelta.h5
Epoch 52/80
1712/1712 [==============================] - 1s 484us/step - loss: 0.0015 - acc: 0.7745 - val_loss: 0.0017 - val_acc: 0.7547

Epoch 00052: val_loss did not improve
Epoch 53/80
1712/1712 [==============================] - 1s 475us/step - loss: 0.0015 - acc: 0.7681 - val_loss: 0.0015 - val_acc: 0.7453

Epoch 00053: val_loss improved from 0.00160 to 0.00155, saving model to my_model_Adadelta.h5
Epoch 54/80
1712/1712 [==============================] - 1s 477us/step - loss: 0.0015 - acc: 0.7652 - val_loss: 0.0016 - val_acc: 0.7593

Epoch 00054: val_loss did not improve
Epoch 55/80
1712/1712 [==============================] - 1s 483us/step - loss: 0.0015 - acc: 0.7558 - val_loss: 0.0018 - val_acc: 0.7430

Epoch 00055: val_loss did not improve
Epoch 56/80
1712/1712 [==============================] - 1s 477us/step - loss: 0.0015 - acc: 0.7734 - val_loss: 0.0015 - val_acc: 0.7477

Epoch 00056: val_loss improved from 0.00155 to 0.00151, saving model to my_model_Adadelta.h5
Epoch 57/80
1712/1712 [==============================] - 1s 476us/step - loss: 0.0014 - acc: 0.7739 - val_loss: 0.0015 - val_acc: 0.7336

Epoch 00057: val_loss did not improve
Epoch 58/80
1712/1712 [==============================] - 1s 469us/step - loss: 0.0014 - acc: 0.7634 - val_loss: 0.0015 - val_acc: 0.7500

Epoch 00058: val_loss improved from 0.00151 to 0.00149, saving model to my_model_Adadelta.h5
Epoch 59/80
1712/1712 [==============================] - 1s 473us/step - loss: 0.0014 - acc: 0.7739 - val_loss: 0.0015 - val_acc: 0.7430

Epoch 00059: val_loss did not improve
Epoch 60/80
1712/1712 [==============================] - 1s 476us/step - loss: 0.0014 - acc: 0.7745 - val_loss: 0.0015 - val_acc: 0.7523

Epoch 00060: val_loss improved from 0.00149 to 0.00147, saving model to my_model_Adadelta.h5
Epoch 61/80
1712/1712 [==============================] - 1s 479us/step - loss: 0.0014 - acc: 0.7728 - val_loss: 0.0015 - val_acc: 0.7547

Epoch 00061: val_loss improved from 0.00147 to 0.00146, saving model to my_model_Adadelta.h5
Epoch 62/80
1712/1712 [==============================] - 1s 480us/step - loss: 0.0014 - acc: 0.7710 - val_loss: 0.0015 - val_acc: 0.7734

Epoch 00062: val_loss improved from 0.00146 to 0.00146, saving model to my_model_Adadelta.h5
Epoch 63/80
1712/1712 [==============================] - 1s 468us/step - loss: 0.0014 - acc: 0.7757 - val_loss: 0.0015 - val_acc: 0.7523

Epoch 00063: val_loss did not improve
Epoch 64/80
1712/1712 [==============================] - 1s 474us/step - loss: 0.0013 - acc: 0.7886 - val_loss: 0.0015 - val_acc: 0.7593

Epoch 00064: val_loss improved from 0.00146 to 0.00146, saving model to my_model_Adadelta.h5
Epoch 65/80
1712/1712 [==============================] - 1s 458us/step - loss: 0.0013 - acc: 0.7775 - val_loss: 0.0014 - val_acc: 0.7780

Epoch 00065: val_loss improved from 0.00146 to 0.00142, saving model to my_model_Adadelta.h5
Epoch 66/80
1712/1712 [==============================] - 1s 466us/step - loss: 0.0013 - acc: 0.7780 - val_loss: 0.0014 - val_acc: 0.7687

Epoch 00066: val_loss improved from 0.00142 to 0.00142, saving model to my_model_Adadelta.h5
Epoch 67/80
1712/1712 [==============================] - 1s 480us/step - loss: 0.0013 - acc: 0.7815 - val_loss: 0.0014 - val_acc: 0.7757

Epoch 00067: val_loss improved from 0.00142 to 0.00139, saving model to my_model_Adadelta.h5
Epoch 68/80
1712/1712 [==============================] - 1s 470us/step - loss: 0.0013 - acc: 0.7757 - val_loss: 0.0014 - val_acc: 0.7757

Epoch 00068: val_loss did not improve
Epoch 69/80
1712/1712 [==============================] - 1s 473us/step - loss: 0.0013 - acc: 0.7810 - val_loss: 0.0014 - val_acc: 0.7734

Epoch 00069: val_loss improved from 0.00139 to 0.00138, saving model to my_model_Adadelta.h5
Epoch 70/80
1712/1712 [==============================] - 1s 472us/step - loss: 0.0013 - acc: 0.7886 - val_loss: 0.0014 - val_acc: 0.7734

Epoch 00070: val_loss did not improve
Epoch 71/80
1712/1712 [==============================] - 1s 480us/step - loss: 0.0013 - acc: 0.7868 - val_loss: 0.0014 - val_acc: 0.7710

Epoch 00071: val_loss did not improve
Epoch 72/80
1712/1712 [==============================] - 1s 478us/step - loss: 0.0013 - acc: 0.7827 - val_loss: 0.0014 - val_acc: 0.7804

Epoch 00072: val_loss did not improve
Epoch 73/80
1712/1712 [==============================] - 1s 468us/step - loss: 0.0012 - acc: 0.7880 - val_loss: 0.0014 - val_acc: 0.7687

Epoch 00073: val_loss did not improve
Epoch 74/80
1712/1712 [==============================] - 1s 472us/step - loss: 0.0013 - acc: 0.7973 - val_loss: 0.0014 - val_acc: 0.7874

Epoch 00074: val_loss did not improve
Epoch 75/80
1712/1712 [==============================] - 1s 473us/step - loss: 0.0013 - acc: 0.7944 - val_loss: 0.0014 - val_acc: 0.7921

Epoch 00075: val_loss improved from 0.00138 to 0.00135, saving model to my_model_Adadelta.h5
Epoch 76/80
1712/1712 [==============================] - 1s 467us/step - loss: 0.0012 - acc: 0.7991 - val_loss: 0.0014 - val_acc: 0.7640

Epoch 00076: val_loss did not improve
Epoch 77/80
1712/1712 [==============================] - 1s 466us/step - loss: 0.0012 - acc: 0.7909 - val_loss: 0.0013 - val_acc: 0.7734

Epoch 00077: val_loss improved from 0.00135 to 0.00133, saving model to my_model_Adadelta.h5
Epoch 78/80
1712/1712 [==============================] - 1s 471us/step - loss: 0.0012 - acc: 0.7944 - val_loss: 0.0013 - val_acc: 0.7897

Epoch 00078: val_loss did not improve
Epoch 79/80
1712/1712 [==============================] - 1s 472us/step - loss: 0.0012 - acc: 0.7856 - val_loss: 0.0014 - val_acc: 0.8014

Epoch 00079: val_loss did not improve
Epoch 80/80
1712/1712 [==============================] - 1s 474us/step - loss: 0.0012 - acc: 0.7903 - val_loss: 0.0013 - val_acc: 0.7804

Epoch 00080: val_loss improved from 0.00133 to 0.00133, saving model to my_model_Adadelta.h5
------------------->Evaluating Adam <----------------------------------------
-----------------------------------------------------------------------------
Train on 1712 samples, validate on 428 samples
Epoch 1/80
1712/1712 [==============================] - 2s 1ms/step - loss: 0.0196 - acc: 0.4393 - val_loss: 0.0048 - val_acc: 0.6963

Epoch 00001: val_loss improved from inf to 0.00484, saving model to my_model_Adam.h5
Epoch 2/80
1712/1712 [==============================] - 1s 460us/step - loss: 0.0080 - acc: 0.6022 - val_loss: 0.0038 - val_acc: 0.6939

Epoch 00002: val_loss improved from 0.00484 to 0.00381, saving model to my_model_Adam.h5
Epoch 3/80
1712/1712 [==============================] - 1s 469us/step - loss: 0.0063 - acc: 0.6192 - val_loss: 0.0037 - val_acc: 0.7009

Epoch 00003: val_loss improved from 0.00381 to 0.00367, saving model to my_model_Adam.h5
Epoch 4/80
1712/1712 [==============================] - 1s 461us/step - loss: 0.0054 - acc: 0.6268 - val_loss: 0.0033 - val_acc: 0.7056

Epoch 00004: val_loss improved from 0.00367 to 0.00329, saving model to my_model_Adam.h5
Epoch 5/80
1712/1712 [==============================] - 1s 455us/step - loss: 0.0046 - acc: 0.6554 - val_loss: 0.0030 - val_acc: 0.7056

Epoch 00005: val_loss improved from 0.00329 to 0.00295, saving model to my_model_Adam.h5
Epoch 6/80
1712/1712 [==============================] - 1s 476us/step - loss: 0.0043 - acc: 0.6665 - val_loss: 0.0029 - val_acc: 0.6986

Epoch 00006: val_loss improved from 0.00295 to 0.00287, saving model to my_model_Adam.h5
Epoch 7/80
1712/1712 [==============================] - 1s 469us/step - loss: 0.0040 - acc: 0.6711 - val_loss: 0.0027 - val_acc: 0.7126

Epoch 00007: val_loss improved from 0.00287 to 0.00266, saving model to my_model_Adam.h5
Epoch 8/80
1712/1712 [==============================] - 1s 470us/step - loss: 0.0037 - acc: 0.6881 - val_loss: 0.0024 - val_acc: 0.7336

Epoch 00008: val_loss improved from 0.00266 to 0.00241, saving model to my_model_Adam.h5
Epoch 9/80
1712/1712 [==============================] - 1s 475us/step - loss: 0.0034 - acc: 0.6723 - val_loss: 0.0023 - val_acc: 0.7266

Epoch 00009: val_loss improved from 0.00241 to 0.00228, saving model to my_model_Adam.h5
Epoch 10/80
1712/1712 [==============================] - 1s 454us/step - loss: 0.0030 - acc: 0.6992 - val_loss: 0.0022 - val_acc: 0.7103

Epoch 00010: val_loss improved from 0.00228 to 0.00224, saving model to my_model_Adam.h5
Epoch 11/80
1712/1712 [==============================] - 1s 464us/step - loss: 0.0030 - acc: 0.7167 - val_loss: 0.0019 - val_acc: 0.7150

Epoch 00011: val_loss improved from 0.00224 to 0.00191, saving model to my_model_Adam.h5
Epoch 12/80
1712/1712 [==============================] - 1s 461us/step - loss: 0.0028 - acc: 0.7068 - val_loss: 0.0020 - val_acc: 0.7477

Epoch 00012: val_loss did not improve
Epoch 13/80
1712/1712 [==============================] - 1s 460us/step - loss: 0.0027 - acc: 0.7150 - val_loss: 0.0018 - val_acc: 0.7243

Epoch 00013: val_loss improved from 0.00191 to 0.00182, saving model to my_model_Adam.h5
Epoch 14/80
1712/1712 [==============================] - 1s 451us/step - loss: 0.0025 - acc: 0.7179 - val_loss: 0.0020 - val_acc: 0.7126

Epoch 00014: val_loss did not improve
Epoch 15/80
1712/1712 [==============================] - 1s 471us/step - loss: 0.0024 - acc: 0.7208 - val_loss: 0.0019 - val_acc: 0.7196

Epoch 00015: val_loss did not improve
Epoch 16/80
1712/1712 [==============================] - 1s 464us/step - loss: 0.0023 - acc: 0.7342 - val_loss: 0.0019 - val_acc: 0.7290

Epoch 00016: val_loss did not improve
Epoch 17/80
1712/1712 [==============================] - 1s 454us/step - loss: 0.0022 - acc: 0.7331 - val_loss: 0.0016 - val_acc: 0.7430

Epoch 00017: val_loss improved from 0.00182 to 0.00158, saving model to my_model_Adam.h5
Epoch 18/80
1712/1712 [==============================] - 1s 467us/step - loss: 0.0021 - acc: 0.7371 - val_loss: 0.0018 - val_acc: 0.7313

Epoch 00018: val_loss did not improve
Epoch 19/80
1712/1712 [==============================] - 1s 463us/step - loss: 0.0020 - acc: 0.7407 - val_loss: 0.0016 - val_acc: 0.7477

Epoch 00019: val_loss improved from 0.00158 to 0.00157, saving model to my_model_Adam.h5
Epoch 20/80
1712/1712 [==============================] - 1s 455us/step - loss: 0.0020 - acc: 0.7296 - val_loss: 0.0016 - val_acc: 0.7430

Epoch 00020: val_loss did not improve
Epoch 21/80
1712/1712 [==============================] - 1s 455us/step - loss: 0.0019 - acc: 0.7371 - val_loss: 0.0015 - val_acc: 0.7500

Epoch 00021: val_loss improved from 0.00157 to 0.00151, saving model to my_model_Adam.h5
Epoch 22/80
1712/1712 [==============================] - 1s 458us/step - loss: 0.0018 - acc: 0.7447 - val_loss: 0.0014 - val_acc: 0.7477

Epoch 00022: val_loss improved from 0.00151 to 0.00144, saving model to my_model_Adam.h5
Epoch 23/80
1712/1712 [==============================] - 1s 465us/step - loss: 0.0017 - acc: 0.7564 - val_loss: 0.0015 - val_acc: 0.7430

Epoch 00023: val_loss did not improve
Epoch 24/80
1712/1712 [==============================] - 1s 462us/step - loss: 0.0016 - acc: 0.7523 - val_loss: 0.0014 - val_acc: 0.7477

Epoch 00024: val_loss did not improve
Epoch 25/80
1712/1712 [==============================] - 1s 465us/step - loss: 0.0016 - acc: 0.7611 - val_loss: 0.0014 - val_acc: 0.7360

Epoch 00025: val_loss improved from 0.00144 to 0.00141, saving model to my_model_Adam.h5
Epoch 26/80
1712/1712 [==============================] - 1s 462us/step - loss: 0.0016 - acc: 0.7658 - val_loss: 0.0013 - val_acc: 0.7430

Epoch 00026: val_loss improved from 0.00141 to 0.00135, saving model to my_model_Adam.h5
Epoch 27/80
1712/1712 [==============================] - 1s 455us/step - loss: 0.0016 - acc: 0.7494 - val_loss: 0.0014 - val_acc: 0.7477

Epoch 00027: val_loss did not improve
Epoch 28/80
1712/1712 [==============================] - 1s 453us/step - loss: 0.0015 - acc: 0.7634 - val_loss: 0.0013 - val_acc: 0.7266

Epoch 00028: val_loss improved from 0.00135 to 0.00135, saving model to my_model_Adam.h5
Epoch 29/80
1712/1712 [==============================] - 1s 471us/step - loss: 0.0015 - acc: 0.7681 - val_loss: 0.0013 - val_acc: 0.7570

Epoch 00029: val_loss improved from 0.00135 to 0.00134, saving model to my_model_Adam.h5
Epoch 30/80
1712/1712 [==============================] - 1s 457us/step - loss: 0.0014 - acc: 0.7716 - val_loss: 0.0013 - val_acc: 0.7593

Epoch 00030: val_loss improved from 0.00134 to 0.00127, saving model to my_model_Adam.h5
Epoch 31/80
1712/1712 [==============================] - 1s 460us/step - loss: 0.0014 - acc: 0.7669 - val_loss: 0.0012 - val_acc: 0.7687

Epoch 00031: val_loss improved from 0.00127 to 0.00125, saving model to my_model_Adam.h5
Epoch 32/80
1712/1712 [==============================] - 1s 459us/step - loss: 0.0013 - acc: 0.7769 - val_loss: 0.0013 - val_acc: 0.7570

Epoch 00032: val_loss did not improve
Epoch 33/80
1712/1712 [==============================] - 1s 467us/step - loss: 0.0013 - acc: 0.7739 - val_loss: 0.0012 - val_acc: 0.7687

Epoch 00033: val_loss improved from 0.00125 to 0.00124, saving model to my_model_Adam.h5
Epoch 34/80
1712/1712 [==============================] - ETA: 0s - loss: 0.0013 - acc: 0.772 - 1s 477us/step - loss: 0.0013 - acc: 0.7739 - val_loss: 0.0013 - val_acc: 0.7500

Epoch 00034: val_loss did not improve
Epoch 35/80
1712/1712 [==============================] - 1s 477us/step - loss: 0.0012 - acc: 0.7821 - val_loss: 0.0012 - val_acc: 0.7593

Epoch 00035: val_loss did not improve
Epoch 36/80
1712/1712 [==============================] - 1s 470us/step - loss: 0.0012 - acc: 0.7722 - val_loss: 0.0012 - val_acc: 0.7593

Epoch 00036: val_loss improved from 0.00124 to 0.00119, saving model to my_model_Adam.h5
Epoch 37/80
1712/1712 [==============================] - 1s 456us/step - loss: 0.0012 - acc: 0.7810 - val_loss: 0.0013 - val_acc: 0.7570

Epoch 00037: val_loss did not improve
Epoch 38/80
1712/1712 [==============================] - 1s 469us/step - loss: 0.0012 - acc: 0.7821 - val_loss: 0.0012 - val_acc: 0.7804

Epoch 00038: val_loss improved from 0.00119 to 0.00118, saving model to my_model_Adam.h5
Epoch 39/80
1712/1712 [==============================] - 1s 457us/step - loss: 0.0012 - acc: 0.7850 - val_loss: 0.0012 - val_acc: 0.7921

Epoch 00039: val_loss improved from 0.00118 to 0.00117, saving model to my_model_Adam.h5
Epoch 40/80
1712/1712 [==============================] - 1s 456us/step - loss: 0.0011 - acc: 0.7915 - val_loss: 0.0012 - val_acc: 0.7757

Epoch 00040: val_loss did not improve
Epoch 41/80
1712/1712 [==============================] - 1s 466us/step - loss: 0.0011 - acc: 0.7868 - val_loss: 0.0012 - val_acc: 0.7640

Epoch 00041: val_loss did not improve
Epoch 42/80
1712/1712 [==============================] - 1s 460us/step - loss: 0.0012 - acc: 0.8008 - val_loss: 0.0012 - val_acc: 0.7827

Epoch 00042: val_loss did not improve
Epoch 43/80
1712/1712 [==============================] - 1s 465us/step - loss: 0.0011 - acc: 0.7926 - val_loss: 0.0012 - val_acc: 0.7757

Epoch 00043: val_loss improved from 0.00117 to 0.00116, saving model to my_model_Adam.h5
Epoch 44/80
1712/1712 [==============================] - 1s 460us/step - loss: 0.0011 - acc: 0.8002 - val_loss: 0.0011 - val_acc: 0.7710

Epoch 00044: val_loss improved from 0.00116 to 0.00115, saving model to my_model_Adam.h5
Epoch 45/80
1712/1712 [==============================] - 1s 475us/step - loss: 0.0011 - acc: 0.7950 - val_loss: 0.0012 - val_acc: 0.7897

Epoch 00045: val_loss did not improve
Epoch 46/80
1712/1712 [==============================] - 1s 477us/step - loss: 0.0010 - acc: 0.8096 - val_loss: 0.0011 - val_acc: 0.7734

Epoch 00046: val_loss improved from 0.00115 to 0.00112, saving model to my_model_Adam.h5
Epoch 47/80
1712/1712 [==============================] - 1s 462us/step - loss: 0.0010 - acc: 0.7961 - val_loss: 0.0011 - val_acc: 0.7617

Epoch 00047: val_loss did not improve
Epoch 48/80
1712/1712 [==============================] - 1s 459us/step - loss: 9.9876e-04 - acc: 0.8072 - val_loss: 0.0011 - val_acc: 0.7734

Epoch 00048: val_loss did not improve
Epoch 49/80
1712/1712 [==============================] - 1s 467us/step - loss: 0.0010 - acc: 0.8143 - val_loss: 0.0011 - val_acc: 0.7757

Epoch 00049: val_loss did not improve
Epoch 50/80
1712/1712 [==============================] - 1s 462us/step - loss: 0.0010 - acc: 0.8055 - val_loss: 0.0011 - val_acc: 0.7687

Epoch 00050: val_loss improved from 0.00112 to 0.00112, saving model to my_model_Adam.h5
Epoch 51/80
1712/1712 [==============================] - 1s 476us/step - loss: 9.7939e-04 - acc: 0.7973 - val_loss: 0.0012 - val_acc: 0.7664

Epoch 00051: val_loss did not improve
Epoch 52/80
1712/1712 [==============================] - 1s 457us/step - loss: 9.6854e-04 - acc: 0.8067 - val_loss: 0.0011 - val_acc: 0.7897

Epoch 00052: val_loss improved from 0.00112 to 0.00111, saving model to my_model_Adam.h5
Epoch 53/80
1712/1712 [==============================] - 1s 468us/step - loss: 9.8585e-04 - acc: 0.8125 - val_loss: 0.0011 - val_acc: 0.7734

Epoch 00053: val_loss improved from 0.00111 to 0.00110, saving model to my_model_Adam.h5
Epoch 54/80
1712/1712 [==============================] - 1s 474us/step - loss: 9.6280e-04 - acc: 0.8119 - val_loss: 0.0011 - val_acc: 0.7757

Epoch 00054: val_loss did not improve
Epoch 55/80
1712/1712 [==============================] - 1s 455us/step - loss: 9.6437e-04 - acc: 0.8172 - val_loss: 0.0011 - val_acc: 0.7710

Epoch 00055: val_loss improved from 0.00110 to 0.00109, saving model to my_model_Adam.h5
Epoch 56/80
1712/1712 [==============================] - 1s 444us/step - loss: 9.5305e-04 - acc: 0.8043 - val_loss: 0.0011 - val_acc: 0.7874

Epoch 00056: val_loss did not improve
Epoch 57/80
1712/1712 [==============================] - 1s 471us/step - loss: 9.4725e-04 - acc: 0.8178 - val_loss: 0.0011 - val_acc: 0.7780

Epoch 00057: val_loss did not improve
Epoch 58/80
1712/1712 [==============================] - 1s 472us/step - loss: 9.2077e-04 - acc: 0.8178 - val_loss: 0.0011 - val_acc: 0.7874

Epoch 00058: val_loss did not improve
Epoch 59/80
1712/1712 [==============================] - 1s 459us/step - loss: 8.9752e-04 - acc: 0.8218 - val_loss: 0.0011 - val_acc: 0.7874

Epoch 00059: val_loss did not improve
Epoch 60/80
1712/1712 [==============================] - 1s 472us/step - loss: 9.0780e-04 - acc: 0.8067 - val_loss: 0.0011 - val_acc: 0.7804

Epoch 00060: val_loss did not improve
Epoch 61/80
1712/1712 [==============================] - 1s 455us/step - loss: 9.1124e-04 - acc: 0.8236 - val_loss: 0.0011 - val_acc: 0.7687

Epoch 00061: val_loss improved from 0.00109 to 0.00108, saving model to my_model_Adam.h5
Epoch 62/80
1712/1712 [==============================] - 1s 449us/step - loss: 9.0752e-04 - acc: 0.8189 - val_loss: 0.0011 - val_acc: 0.7734

Epoch 00062: val_loss did not improve
Epoch 63/80
1712/1712 [==============================] - 1s 463us/step - loss: 9.3439e-04 - acc: 0.8195 - val_loss: 0.0011 - val_acc: 0.7780

Epoch 00063: val_loss did not improve
Epoch 64/80
1712/1712 [==============================] - 1s 461us/step - loss: 9.0890e-04 - acc: 0.8224 - val_loss: 0.0011 - val_acc: 0.7827

Epoch 00064: val_loss did not improve
Epoch 65/80
1712/1712 [==============================] - 1s 470us/step - loss: 8.9048e-04 - acc: 0.8014 - val_loss: 0.0011 - val_acc: 0.7944

Epoch 00065: val_loss did not improve
Epoch 66/80
1712/1712 [==============================] - 1s 480us/step - loss: 8.9936e-04 - acc: 0.8113 - val_loss: 0.0011 - val_acc: 0.7850

Epoch 00066: val_loss did not improve
Epoch 67/80
1712/1712 [==============================] - 1s 472us/step - loss: 8.7626e-04 - acc: 0.8230 - val_loss: 0.0011 - val_acc: 0.7874

Epoch 00067: val_loss did not improve
Epoch 68/80
1712/1712 [==============================] - 1s 451us/step - loss: 8.4106e-04 - acc: 0.8271 - val_loss: 0.0011 - val_acc: 0.7921

Epoch 00068: val_loss did not improve
Epoch 69/80
1712/1712 [==============================] - 1s 460us/step - loss: 8.5899e-04 - acc: 0.8271 - val_loss: 0.0011 - val_acc: 0.7850

Epoch 00069: val_loss improved from 0.00108 to 0.00105, saving model to my_model_Adam.h5
Epoch 70/80
1712/1712 [==============================] - 1s 463us/step - loss: 8.6030e-04 - acc: 0.8137 - val_loss: 0.0011 - val_acc: 0.7850

Epoch 00070: val_loss did not improve
Epoch 71/80
1712/1712 [==============================] - 1s 465us/step - loss: 8.4626e-04 - acc: 0.8376 - val_loss: 0.0011 - val_acc: 0.7921

Epoch 00071: val_loss did not improve
Epoch 72/80
1712/1712 [==============================] - 1s 468us/step - loss: 8.3546e-04 - acc: 0.8347 - val_loss: 0.0011 - val_acc: 0.7874

Epoch 00072: val_loss did not improve
Epoch 73/80
1712/1712 [==============================] - 1s 465us/step - loss: 8.4262e-04 - acc: 0.8213 - val_loss: 0.0011 - val_acc: 0.7921

Epoch 00073: val_loss did not improve
Epoch 74/80
1712/1712 [==============================] - 1s 472us/step - loss: 8.6026e-04 - acc: 0.8119 - val_loss: 0.0011 - val_acc: 0.7967

Epoch 00074: val_loss did not improve
Epoch 75/80
1712/1712 [==============================] - 1s 456us/step - loss: 8.0431e-04 - acc: 0.8213 - val_loss: 0.0011 - val_acc: 0.7991

Epoch 00075: val_loss did not improve
Epoch 76/80
1712/1712 [==============================] - 1s 467us/step - loss: 8.7953e-04 - acc: 0.8224 - val_loss: 0.0010 - val_acc: 0.7897

Epoch 00076: val_loss improved from 0.00105 to 0.00104, saving model to my_model_Adam.h5
Epoch 77/80
1712/1712 [==============================] - 1s 476us/step - loss: 8.1410e-04 - acc: 0.8224 - val_loss: 0.0011 - val_acc: 0.7944

Epoch 00077: val_loss did not improve
Epoch 78/80
1712/1712 [==============================] - 1s 463us/step - loss: 8.1593e-04 - acc: 0.8359 - val_loss: 0.0011 - val_acc: 0.7967

Epoch 00078: val_loss did not improve
Epoch 79/80
1712/1712 [==============================] - 1s 456us/step - loss: 8.2789e-04 - acc: 0.8230 - val_loss: 0.0011 - val_acc: 0.7944

Epoch 00079: val_loss did not improve
Epoch 80/80
1712/1712 [==============================] - 1s 461us/step - loss: 8.2088e-04 - acc: 0.8283 - val_loss: 0.0011 - val_acc: 0.8014

Epoch 00080: val_loss did not improve
------------------->Evaluating Adamax <----------------------------------------
-----------------------------------------------------------------------------
Train on 1712 samples, validate on 428 samples
Epoch 1/80
1712/1712 [==============================] - 2s 1ms/step - loss: 0.0227 - acc: 0.3902 - val_loss: 0.0045 - val_acc: 0.6963

Epoch 00001: val_loss improved from inf to 0.00445, saving model to my_model_Adamax.h5
Epoch 2/80
1712/1712 [==============================] - 1s 441us/step - loss: 0.0093 - acc: 0.5158 - val_loss: 0.0041 - val_acc: 0.6963

Epoch 00002: val_loss improved from 0.00445 to 0.00410, saving model to my_model_Adamax.h5
Epoch 3/80
1712/1712 [==============================] - 1s 448us/step - loss: 0.0078 - acc: 0.5707 - val_loss: 0.0040 - val_acc: 0.6986

Epoch 00003: val_loss improved from 0.00410 to 0.00404, saving model to my_model_Adamax.h5
Epoch 4/80
1712/1712 [==============================] - 1s 451us/step - loss: 0.0069 - acc: 0.5935 - val_loss: 0.0041 - val_acc: 0.7009

Epoch 00004: val_loss did not improve
Epoch 5/80
1712/1712 [==============================] - 1s 442us/step - loss: 0.0066 - acc: 0.5917 - val_loss: 0.0034 - val_acc: 0.7056

Epoch 00005: val_loss improved from 0.00404 to 0.00336, saving model to my_model_Adamax.h5
Epoch 6/80
1712/1712 [==============================] - 1s 438us/step - loss: 0.0061 - acc: 0.6238 - val_loss: 0.0033 - val_acc: 0.7033

Epoch 00006: val_loss improved from 0.00336 to 0.00331, saving model to my_model_Adamax.h5
Epoch 7/80
1712/1712 [==============================] - 1s 452us/step - loss: 0.0056 - acc: 0.6332 - val_loss: 0.0038 - val_acc: 0.7056

Epoch 00007: val_loss did not improve
Epoch 8/80
1712/1712 [==============================] - 1s 457us/step - loss: 0.0053 - acc: 0.6262 - val_loss: 0.0029 - val_acc: 0.6963

Epoch 00008: val_loss improved from 0.00331 to 0.00294, saving model to my_model_Adamax.h5
Epoch 9/80
1712/1712 [==============================] - 1s 448us/step - loss: 0.0049 - acc: 0.6197 - val_loss: 0.0027 - val_acc: 0.7056

Epoch 00009: val_loss improved from 0.00294 to 0.00273, saving model to my_model_Adamax.h5
Epoch 10/80
1712/1712 [==============================] - 1s 445us/step - loss: 0.0049 - acc: 0.6437 - val_loss: 0.0029 - val_acc: 0.6986

Epoch 00010: val_loss did not improve
Epoch 11/80
1712/1712 [==============================] - 1s 441us/step - loss: 0.0045 - acc: 0.6303 - val_loss: 0.0025 - val_acc: 0.7009

Epoch 00011: val_loss improved from 0.00273 to 0.00251, saving model to my_model_Adamax.h5
Epoch 12/80
1712/1712 [==============================] - 1s 444us/step - loss: 0.0044 - acc: 0.6536 - val_loss: 0.0024 - val_acc: 0.6939

Epoch 00012: val_loss improved from 0.00251 to 0.00243, saving model to my_model_Adamax.h5
Epoch 13/80
1712/1712 [==============================] - 1s 458us/step - loss: 0.0039 - acc: 0.6519 - val_loss: 0.0023 - val_acc: 0.6986

Epoch 00013: val_loss improved from 0.00243 to 0.00233, saving model to my_model_Adamax.h5
Epoch 14/80
1712/1712 [==============================] - 1s 451us/step - loss: 0.0040 - acc: 0.6577 - val_loss: 0.0023 - val_acc: 0.6986

Epoch 00014: val_loss improved from 0.00233 to 0.00227, saving model to my_model_Adamax.h5
Epoch 15/80
1712/1712 [==============================] - 1s 443us/step - loss: 0.0037 - acc: 0.6746 - val_loss: 0.0023 - val_acc: 0.6986

Epoch 00015: val_loss did not improve
Epoch 16/80
1712/1712 [==============================] - 1s 443us/step - loss: 0.0035 - acc: 0.6828 - val_loss: 0.0021 - val_acc: 0.7079

Epoch 00016: val_loss improved from 0.00227 to 0.00212, saving model to my_model_Adamax.h5
Epoch 17/80
1712/1712 [==============================] - 1s 441us/step - loss: 0.0034 - acc: 0.6986 - val_loss: 0.0021 - val_acc: 0.7103

Epoch 00017: val_loss improved from 0.00212 to 0.00205, saving model to my_model_Adamax.h5
Epoch 18/80
1712/1712 [==============================] - 1s 443us/step - loss: 0.0032 - acc: 0.7050 - val_loss: 0.0019 - val_acc: 0.7056

Epoch 00018: val_loss improved from 0.00205 to 0.00192, saving model to my_model_Adamax.h5
Epoch 19/80
1712/1712 [==============================] - 1s 449us/step - loss: 0.0031 - acc: 0.6968 - val_loss: 0.0019 - val_acc: 0.7196

Epoch 00019: val_loss improved from 0.00192 to 0.00186, saving model to my_model_Adamax.h5
Epoch 20/80
1712/1712 [==============================] - 1s 452us/step - loss: 0.0029 - acc: 0.6933 - val_loss: 0.0019 - val_acc: 0.7266

Epoch 00020: val_loss did not improve
Epoch 21/80
1712/1712 [==============================] - 1s 453us/step - loss: 0.0030 - acc: 0.7062 - val_loss: 0.0018 - val_acc: 0.7056

Epoch 00021: val_loss improved from 0.00186 to 0.00178, saving model to my_model_Adamax.h5
Epoch 22/80
1712/1712 [==============================] - 1s 453us/step - loss: 0.0027 - acc: 0.7091 - val_loss: 0.0017 - val_acc: 0.7313

Epoch 00022: val_loss improved from 0.00178 to 0.00172, saving model to my_model_Adamax.h5
Epoch 23/80
1712/1712 [==============================] - 1s 448us/step - loss: 0.0025 - acc: 0.7150 - val_loss: 0.0019 - val_acc: 0.7336

Epoch 00023: val_loss did not improve
Epoch 24/80
1712/1712 [==============================] - 1s 460us/step - loss: 0.0026 - acc: 0.7225 - val_loss: 0.0018 - val_acc: 0.7360

Epoch 00024: val_loss did not improve
Epoch 25/80
1712/1712 [==============================] - 1s 445us/step - loss: 0.0023 - acc: 0.7261 - val_loss: 0.0017 - val_acc: 0.7360

Epoch 00025: val_loss improved from 0.00172 to 0.00167, saving model to my_model_Adamax.h5
Epoch 26/80
1712/1712 [==============================] - 1s 437us/step - loss: 0.0023 - acc: 0.7395 - val_loss: 0.0017 - val_acc: 0.7383

Epoch 00026: val_loss did not improve
Epoch 27/80
1712/1712 [==============================] - 1s 450us/step - loss: 0.0022 - acc: 0.7261 - val_loss: 0.0015 - val_acc: 0.7430

Epoch 00027: val_loss improved from 0.00167 to 0.00152, saving model to my_model_Adamax.h5
Epoch 28/80
1712/1712 [==============================] - 1s 446us/step - loss: 0.0020 - acc: 0.7523 - val_loss: 0.0015 - val_acc: 0.7500

Epoch 00028: val_loss improved from 0.00152 to 0.00147, saving model to my_model_Adamax.h5
Epoch 29/80
1712/1712 [==============================] - 1s 443us/step - loss: 0.0020 - acc: 0.7395 - val_loss: 0.0016 - val_acc: 0.7453

Epoch 00029: val_loss did not improve
Epoch 30/80
1712/1712 [==============================] - 1s 445us/step - loss: 0.0020 - acc: 0.7494 - val_loss: 0.0015 - val_acc: 0.7570

Epoch 00030: val_loss did not improve
Epoch 31/80
1712/1712 [==============================] - 1s 442us/step - loss: 0.0019 - acc: 0.7471 - val_loss: 0.0014 - val_acc: 0.7570

Epoch 00031: val_loss improved from 0.00147 to 0.00143, saving model to my_model_Adamax.h5
Epoch 32/80
1712/1712 [==============================] - 1s 442us/step - loss: 0.0017 - acc: 0.7599 - val_loss: 0.0014 - val_acc: 0.7734

Epoch 00032: val_loss improved from 0.00143 to 0.00143, saving model to my_model_Adamax.h5
Epoch 33/80
1712/1712 [==============================] - 1s 452us/step - loss: 0.0018 - acc: 0.7623 - val_loss: 0.0014 - val_acc: 0.7523

Epoch 00033: val_loss improved from 0.00143 to 0.00142, saving model to my_model_Adamax.h5
Epoch 34/80
1712/1712 [==============================] - 1s 457us/step - loss: 0.0017 - acc: 0.7664 - val_loss: 0.0014 - val_acc: 0.7453

Epoch 00034: val_loss did not improve
Epoch 35/80
1712/1712 [==============================] - 1s 439us/step - loss: 0.0016 - acc: 0.7664 - val_loss: 0.0015 - val_acc: 0.7687

Epoch 00035: val_loss did not improve
Epoch 36/80
1712/1712 [==============================] - 1s 468us/step - loss: 0.0015 - acc: 0.7658 - val_loss: 0.0013 - val_acc: 0.7664

Epoch 00036: val_loss improved from 0.00142 to 0.00134, saving model to my_model_Adamax.h5
Epoch 37/80
1712/1712 [==============================] - 1s 469us/step - loss: 0.0015 - acc: 0.7646 - val_loss: 0.0014 - val_acc: 0.7570

Epoch 00037: val_loss did not improve
Epoch 38/80
1712/1712 [==============================] - 1s 456us/step - loss: 0.0014 - acc: 0.7693 - val_loss: 0.0014 - val_acc: 0.7734

Epoch 00038: val_loss did not improve
Epoch 39/80
1712/1712 [==============================] - 1s 445us/step - loss: 0.0014 - acc: 0.7699 - val_loss: 0.0012 - val_acc: 0.7757

Epoch 00039: val_loss improved from 0.00134 to 0.00124, saving model to my_model_Adamax.h5
Epoch 40/80
1712/1712 [==============================] - 1s 455us/step - loss: 0.0013 - acc: 0.7991 - val_loss: 0.0013 - val_acc: 0.7640

Epoch 00040: val_loss did not improve
Epoch 41/80
1712/1712 [==============================] - 1s 444us/step - loss: 0.0013 - acc: 0.7810 - val_loss: 0.0013 - val_acc: 0.7710

Epoch 00041: val_loss did not improve
Epoch 42/80
1712/1712 [==============================] - 1s 440us/step - loss: 0.0012 - acc: 0.7915 - val_loss: 0.0013 - val_acc: 0.7710

Epoch 00042: val_loss did not improve
Epoch 43/80
1712/1712 [==============================] - 1s 454us/step - loss: 0.0012 - acc: 0.7909 - val_loss: 0.0012 - val_acc: 0.7664

Epoch 00043: val_loss improved from 0.00124 to 0.00121, saving model to my_model_Adamax.h5
Epoch 44/80
1712/1712 [==============================] - 1s 455us/step - loss: 0.0012 - acc: 0.8043 - val_loss: 0.0012 - val_acc: 0.7944

Epoch 00044: val_loss did not improve
Epoch 45/80
1712/1712 [==============================] - 1s 441us/step - loss: 0.0012 - acc: 0.7991 - val_loss: 0.0012 - val_acc: 0.7991

Epoch 00045: val_loss improved from 0.00121 to 0.00115, saving model to my_model_Adamax.h5
Epoch 46/80
1712/1712 [==============================] - 1s 451us/step - loss: 0.0012 - acc: 0.7979 - val_loss: 0.0012 - val_acc: 0.7944

Epoch 00046: val_loss did not improve
Epoch 47/80
1712/1712 [==============================] - 1s 439us/step - loss: 0.0011 - acc: 0.7950 - val_loss: 0.0012 - val_acc: 0.7921

Epoch 00047: val_loss did not improve
Epoch 48/80
1712/1712 [==============================] - 1s 448us/step - loss: 0.0011 - acc: 0.8055 - val_loss: 0.0012 - val_acc: 0.7850

Epoch 00048: val_loss did not improve
Epoch 49/80
1712/1712 [==============================] - 1s 449us/step - loss: 0.0011 - acc: 0.7950 - val_loss: 0.0011 - val_acc: 0.7967

Epoch 00049: val_loss improved from 0.00115 to 0.00114, saving model to my_model_Adamax.h5
Epoch 50/80
1712/1712 [==============================] - 1s 445us/step - loss: 0.0011 - acc: 0.8090 - val_loss: 0.0012 - val_acc: 0.7921

Epoch 00050: val_loss did not improve
Epoch 51/80
1712/1712 [==============================] - 1s 441us/step - loss: 0.0011 - acc: 0.8055 - val_loss: 0.0011 - val_acc: 0.7991

Epoch 00051: val_loss improved from 0.00114 to 0.00113, saving model to my_model_Adamax.h5
Epoch 52/80
1712/1712 [==============================] - 1s 450us/step - loss: 0.0010 - acc: 0.8067 - val_loss: 0.0011 - val_acc: 0.7850

Epoch 00052: val_loss improved from 0.00113 to 0.00111, saving model to my_model_Adamax.h5
Epoch 53/80
1712/1712 [==============================] - 1s 441us/step - loss: 9.7671e-04 - acc: 0.8096 - val_loss: 0.0011 - val_acc: 0.7967

Epoch 00053: val_loss improved from 0.00111 to 0.00110, saving model to my_model_Adamax.h5
Epoch 54/80
1712/1712 [==============================] - 1s 452us/step - loss: 9.8551e-04 - acc: 0.8178 - val_loss: 0.0011 - val_acc: 0.7921

Epoch 00054: val_loss did not improve
Epoch 55/80
1712/1712 [==============================] - 1s 441us/step - loss: 9.5662e-04 - acc: 0.8131 - val_loss: 0.0011 - val_acc: 0.7991

Epoch 00055: val_loss improved from 0.00110 to 0.00108, saving model to my_model_Adamax.h5
Epoch 56/80
1712/1712 [==============================] - 1s 455us/step - loss: 9.3085e-04 - acc: 0.8201 - val_loss: 0.0011 - val_acc: 0.7967

Epoch 00056: val_loss did not improve
Epoch 57/80
1712/1712 [==============================] - 1s 451us/step - loss: 9.0877e-04 - acc: 0.8230 - val_loss: 0.0011 - val_acc: 0.7897

Epoch 00057: val_loss improved from 0.00108 to 0.00107, saving model to my_model_Adamax.h5
Epoch 58/80
1712/1712 [==============================] - 1s 455us/step - loss: 9.3023e-04 - acc: 0.8154 - val_loss: 0.0011 - val_acc: 0.7991

Epoch 00058: val_loss did not improve
Epoch 59/80
1712/1712 [==============================] - 1s 460us/step - loss: 9.1146e-04 - acc: 0.8107 - val_loss: 0.0011 - val_acc: 0.7874

Epoch 00059: val_loss did not improve
Epoch 60/80
1712/1712 [==============================] - 1s 445us/step - loss: 8.9275e-04 - acc: 0.8318 - val_loss: 0.0011 - val_acc: 0.7921

Epoch 00060: val_loss did not improve
Epoch 61/80
1712/1712 [==============================] - 1s 468us/step - loss: 8.2916e-04 - acc: 0.8224 - val_loss: 0.0011 - val_acc: 0.7874

Epoch 00061: val_loss did not improve
Epoch 62/80
1712/1712 [==============================] - 1s 453us/step - loss: 8.2516e-04 - acc: 0.8318 - val_loss: 0.0011 - val_acc: 0.7897

Epoch 00062: val_loss did not improve
Epoch 63/80
1712/1712 [==============================] - 1s 441us/step - loss: 8.3090e-04 - acc: 0.8265 - val_loss: 0.0011 - val_acc: 0.7967

Epoch 00063: val_loss did not improve
Epoch 64/80
1712/1712 [==============================] - 1s 450us/step - loss: 8.2664e-04 - acc: 0.8312 - val_loss: 0.0011 - val_acc: 0.8037

Epoch 00064: val_loss improved from 0.00107 to 0.00105, saving model to my_model_Adamax.h5
Epoch 65/80
1712/1712 [==============================] - 1s 460us/step - loss: 8.0598e-04 - acc: 0.8224 - val_loss: 0.0010 - val_acc: 0.8037

Epoch 00065: val_loss improved from 0.00105 to 0.00103, saving model to my_model_Adamax.h5
Epoch 66/80
1712/1712 [==============================] - 1s 456us/step - loss: 8.0667e-04 - acc: 0.8283 - val_loss: 0.0010 - val_acc: 0.8107

Epoch 00066: val_loss did not improve
Epoch 67/80
1712/1712 [==============================] - 1s 448us/step - loss: 8.1768e-04 - acc: 0.8283 - val_loss: 0.0010 - val_acc: 0.8037

Epoch 00067: val_loss improved from 0.00103 to 0.00103, saving model to my_model_Adamax.h5
Epoch 68/80
1712/1712 [==============================] - 1s 444us/step - loss: 8.0812e-04 - acc: 0.8324 - val_loss: 0.0010 - val_acc: 0.8131

Epoch 00068: val_loss did not improve
Epoch 69/80
1712/1712 [==============================] - 1s 453us/step - loss: 7.8829e-04 - acc: 0.8353 - val_loss: 0.0010 - val_acc: 0.7944

Epoch 00069: val_loss improved from 0.00103 to 0.00102, saving model to my_model_Adamax.h5
Epoch 70/80
1712/1712 [==============================] - 1s 448us/step - loss: 7.9078e-04 - acc: 0.8300 - val_loss: 0.0011 - val_acc: 0.8037

Epoch 00070: val_loss did not improve
Epoch 71/80
1712/1712 [==============================] - 1s 437us/step - loss: 7.7844e-04 - acc: 0.8300 - val_loss: 0.0011 - val_acc: 0.8014

Epoch 00071: val_loss did not improve
Epoch 72/80
1712/1712 [==============================] - 1s 465us/step - loss: 7.5673e-04 - acc: 0.8195 - val_loss: 0.0010 - val_acc: 0.8154

Epoch 00072: val_loss did not improve
Epoch 73/80
1712/1712 [==============================] - 1s 449us/step - loss: 7.3890e-04 - acc: 0.8458 - val_loss: 0.0011 - val_acc: 0.8084

Epoch 00073: val_loss did not improve
Epoch 74/80
1712/1712 [==============================] - 1s 452us/step - loss: 7.7839e-04 - acc: 0.8347 - val_loss: 0.0010 - val_acc: 0.8178

Epoch 00074: val_loss did not improve
Epoch 75/80
1712/1712 [==============================] - 1s 448us/step - loss: 7.1769e-04 - acc: 0.8464 - val_loss: 0.0010 - val_acc: 0.8154

Epoch 00075: val_loss did not improve
Epoch 76/80
1712/1712 [==============================] - 1s 447us/step - loss: 7.3144e-04 - acc: 0.8458 - val_loss: 0.0010 - val_acc: 0.7757

Epoch 00076: val_loss did not improve
Epoch 77/80
1712/1712 [==============================] - 1s 445us/step - loss: 7.0665e-04 - acc: 0.8294 - val_loss: 0.0010 - val_acc: 0.8037

Epoch 00077: val_loss did not improve
Epoch 78/80
1712/1712 [==============================] - 1s 448us/step - loss: 6.9563e-04 - acc: 0.8446 - val_loss: 0.0011 - val_acc: 0.7921

Epoch 00078: val_loss did not improve
Epoch 79/80
1712/1712 [==============================] - 1s 462us/step - loss: 6.8829e-04 - acc: 0.8516 - val_loss: 0.0010 - val_acc: 0.8014

Epoch 00079: val_loss improved from 0.00102 to 0.00101, saving model to my_model_Adamax.h5
Epoch 80/80
1712/1712 [==============================] - 1s 437us/step - loss: 6.9024e-04 - acc: 0.8475 - val_loss: 0.0010 - val_acc: 0.8084

Epoch 00080: val_loss did not improve
------------------->Evaluating Nadam <----------------------------------------
-----------------------------------------------------------------------------
Train on 1712 samples, validate on 428 samples
Epoch 1/80
1712/1712 [==============================] - 2s 1ms/step - loss: 0.0236 - acc: 0.4574 - val_loss: 0.0044 - val_acc: 0.6963

Epoch 00001: val_loss improved from inf to 0.00442, saving model to my_model_Nadam.h5
Epoch 2/80
1712/1712 [==============================] - 1s 475us/step - loss: 0.0085 - acc: 0.6057 - val_loss: 0.0062 - val_acc: 0.6963

Epoch 00002: val_loss did not improve
Epoch 3/80
1712/1712 [==============================] - 1s 474us/step - loss: 0.0071 - acc: 0.6314 - val_loss: 0.0039 - val_acc: 0.7009

Epoch 00003: val_loss improved from 0.00442 to 0.00390, saving model to my_model_Nadam.h5
Epoch 4/80
1712/1712 [==============================] - 1s 476us/step - loss: 0.0057 - acc: 0.6536 - val_loss: 0.0035 - val_acc: 0.7009

Epoch 00004: val_loss improved from 0.00390 to 0.00354, saving model to my_model_Nadam.h5
Epoch 5/80
1712/1712 [==============================] - 1s 490us/step - loss: 0.0050 - acc: 0.6746 - val_loss: 0.0031 - val_acc: 0.7150

Epoch 00005: val_loss improved from 0.00354 to 0.00306, saving model to my_model_Nadam.h5
Epoch 6/80
1712/1712 [==============================] - 1s 469us/step - loss: 0.0043 - acc: 0.6682 - val_loss: 0.0029 - val_acc: 0.6986

Epoch 00006: val_loss improved from 0.00306 to 0.00290, saving model to my_model_Nadam.h5
Epoch 7/80
1712/1712 [==============================] - 1s 479us/step - loss: 0.0036 - acc: 0.7167 - val_loss: 0.0026 - val_acc: 0.7173

Epoch 00007: val_loss improved from 0.00290 to 0.00259, saving model to my_model_Nadam.h5
Epoch 8/80
1712/1712 [==============================] - 1s 494us/step - loss: 0.0032 - acc: 0.7056 - val_loss: 0.0030 - val_acc: 0.7313

Epoch 00008: val_loss did not improve
Epoch 9/80
1712/1712 [==============================] - 1s 473us/step - loss: 0.0029 - acc: 0.7249 - val_loss: 0.0023 - val_acc: 0.7313

Epoch 00009: val_loss improved from 0.00259 to 0.00231, saving model to my_model_Nadam.h5
Epoch 10/80
1712/1712 [==============================] - 1s 477us/step - loss: 0.0027 - acc: 0.7249 - val_loss: 0.0019 - val_acc: 0.7266

Epoch 00010: val_loss improved from 0.00231 to 0.00193, saving model to my_model_Nadam.h5
Epoch 11/80
1712/1712 [==============================] - 1s 487us/step - loss: 0.0024 - acc: 0.7377 - val_loss: 0.0018 - val_acc: 0.7266

Epoch 00011: val_loss improved from 0.00193 to 0.00180, saving model to my_model_Nadam.h5
Epoch 12/80
1712/1712 [==============================] - 1s 482us/step - loss: 0.0021 - acc: 0.7442 - val_loss: 0.0016 - val_acc: 0.7360

Epoch 00012: val_loss improved from 0.00180 to 0.00164, saving model to my_model_Nadam.h5
Epoch 13/80
1712/1712 [==============================] - 1s 476us/step - loss: 0.0021 - acc: 0.7436 - val_loss: 0.0017 - val_acc: 0.7290

Epoch 00013: val_loss did not improve
Epoch 14/80
1712/1712 [==============================] - 1s 482us/step - loss: 0.0019 - acc: 0.7430 - val_loss: 0.0019 - val_acc: 0.7407

Epoch 00014: val_loss did not improve
Epoch 15/80
1712/1712 [==============================] - 1s 471us/step - loss: 0.0018 - acc: 0.7716 - val_loss: 0.0016 - val_acc: 0.7313

Epoch 00015: val_loss improved from 0.00164 to 0.00160, saving model to my_model_Nadam.h5
Epoch 16/80
1712/1712 [==============================] - 1s 478us/step - loss: 0.0017 - acc: 0.7669 - val_loss: 0.0014 - val_acc: 0.7500

Epoch 00016: val_loss improved from 0.00160 to 0.00145, saving model to my_model_Nadam.h5
Epoch 17/80
1712/1712 [==============================] - 1s 490us/step - loss: 0.0015 - acc: 0.7775 - val_loss: 0.0014 - val_acc: 0.7757

Epoch 00017: val_loss improved from 0.00145 to 0.00139, saving model to my_model_Nadam.h5
Epoch 18/80
1712/1712 [==============================] - 1s 486us/step - loss: 0.0015 - acc: 0.7798 - val_loss: 0.0015 - val_acc: 0.7780

Epoch 00018: val_loss did not improve
Epoch 19/80
1712/1712 [==============================] - 1s 474us/step - loss: 0.0015 - acc: 0.7804 - val_loss: 0.0013 - val_acc: 0.7687

Epoch 00019: val_loss improved from 0.00139 to 0.00134, saving model to my_model_Nadam.h5
Epoch 20/80
1712/1712 [==============================] - 1s 472us/step - loss: 0.0014 - acc: 0.7833 - val_loss: 0.0013 - val_acc: 0.7850

Epoch 00020: val_loss improved from 0.00134 to 0.00133, saving model to my_model_Nadam.h5
Epoch 21/80
1712/1712 [==============================] - 1s 488us/step - loss: 0.0013 - acc: 0.7833 - val_loss: 0.0013 - val_acc: 0.7827

Epoch 00021: val_loss improved from 0.00133 to 0.00126, saving model to my_model_Nadam.h5
Epoch 22/80
1712/1712 [==============================] - 1s 466us/step - loss: 0.0013 - acc: 0.7827 - val_loss: 0.0013 - val_acc: 0.7593

Epoch 00022: val_loss did not improve
Epoch 23/80
1712/1712 [==============================] - 1s 473us/step - loss: 0.0013 - acc: 0.7944 - val_loss: 0.0013 - val_acc: 0.7944

Epoch 00023: val_loss did not improve
Epoch 24/80
1712/1712 [==============================] - 1s 489us/step - loss: 0.0012 - acc: 0.7956 - val_loss: 0.0013 - val_acc: 0.7967

Epoch 00024: val_loss did not improve
Epoch 25/80
1712/1712 [==============================] - 1s 476us/step - loss: 0.0012 - acc: 0.8102 - val_loss: 0.0015 - val_acc: 0.7897

Epoch 00025: val_loss did not improve
Epoch 26/80
1712/1712 [==============================] - 1s 487us/step - loss: 0.0011 - acc: 0.8160 - val_loss: 0.0012 - val_acc: 0.7967

Epoch 00026: val_loss improved from 0.00126 to 0.00122, saving model to my_model_Nadam.h5
Epoch 27/80
1712/1712 [==============================] - 1s 481us/step - loss: 0.0011 - acc: 0.8137 - val_loss: 0.0012 - val_acc: 0.7944

Epoch 00027: val_loss did not improve
Epoch 28/80
1712/1712 [==============================] - 1s 477us/step - loss: 0.0011 - acc: 0.8049 - val_loss: 0.0012 - val_acc: 0.7921

Epoch 00028: val_loss improved from 0.00122 to 0.00120, saving model to my_model_Nadam.h5
Epoch 29/80
1712/1712 [==============================] - 1s 475us/step - loss: 0.0011 - acc: 0.8201 - val_loss: 0.0015 - val_acc: 0.7967

Epoch 00029: val_loss did not improve
Epoch 30/80
1712/1712 [==============================] - 1s 477us/step - loss: 0.0011 - acc: 0.8148 - val_loss: 0.0012 - val_acc: 0.7874

Epoch 00030: val_loss improved from 0.00120 to 0.00118, saving model to my_model_Nadam.h5
Epoch 31/80
1712/1712 [==============================] - 1s 483us/step - loss: 0.0010 - acc: 0.8195 - val_loss: 0.0011 - val_acc: 0.8084

Epoch 00031: val_loss improved from 0.00118 to 0.00114, saving model to my_model_Nadam.h5
Epoch 32/80
1712/1712 [==============================] - 1s 477us/step - loss: 9.8015e-04 - acc: 0.8078 - val_loss: 0.0012 - val_acc: 0.7874

Epoch 00032: val_loss did not improve
Epoch 33/80
1712/1712 [==============================] - 1s 479us/step - loss: 9.5360e-04 - acc: 0.8213 - val_loss: 0.0011 - val_acc: 0.8084

Epoch 00033: val_loss improved from 0.00114 to 0.00113, saving model to my_model_Nadam.h5
Epoch 34/80
1712/1712 [==============================] - 1s 489us/step - loss: 9.3557e-04 - acc: 0.8131 - val_loss: 0.0011 - val_acc: 0.7897

Epoch 00034: val_loss improved from 0.00113 to 0.00113, saving model to my_model_Nadam.h5
Epoch 35/80
1712/1712 [==============================] - 1s 483us/step - loss: 9.4645e-04 - acc: 0.8207 - val_loss: 0.0011 - val_acc: 0.7991

Epoch 00035: val_loss did not improve
Epoch 36/80
1712/1712 [==============================] - 1s 482us/step - loss: 9.1896e-04 - acc: 0.8213 - val_loss: 0.0011 - val_acc: 0.8037

Epoch 00036: val_loss did not improve
Epoch 37/80
1712/1712 [==============================] - 1s 470us/step - loss: 9.0032e-04 - acc: 0.8254 - val_loss: 0.0012 - val_acc: 0.8014

Epoch 00037: val_loss did not improve
Epoch 38/80
1712/1712 [==============================] - 1s 474us/step - loss: 9.0239e-04 - acc: 0.8189 - val_loss: 0.0011 - val_acc: 0.8037

Epoch 00038: val_loss improved from 0.00113 to 0.00110, saving model to my_model_Nadam.h5
Epoch 39/80
1712/1712 [==============================] - 1s 481us/step - loss: 8.8598e-04 - acc: 0.8388 - val_loss: 0.0011 - val_acc: 0.8084

Epoch 00039: val_loss improved from 0.00110 to 0.00110, saving model to my_model_Nadam.h5
Epoch 40/80
1712/1712 [==============================] - 1s 469us/step - loss: 8.9105e-04 - acc: 0.8347 - val_loss: 0.0012 - val_acc: 0.7921

Epoch 00040: val_loss did not improve
Epoch 41/80
1712/1712 [==============================] - 1s 489us/step - loss: 8.7642e-04 - acc: 0.8259 - val_loss: 0.0012 - val_acc: 0.7967

Epoch 00041: val_loss did not improve
Epoch 42/80
1712/1712 [==============================] - 1s 486us/step - loss: 8.3955e-04 - acc: 0.8259 - val_loss: 0.0011 - val_acc: 0.7944

Epoch 00042: val_loss did not improve
Epoch 43/80
1712/1712 [==============================] - 1s 482us/step - loss: 8.5331e-04 - acc: 0.8324 - val_loss: 0.0012 - val_acc: 0.7967

Epoch 00043: val_loss did not improve
Epoch 44/80
1712/1712 [==============================] - 1s 481us/step - loss: 8.0989e-04 - acc: 0.8335 - val_loss: 0.0012 - val_acc: 0.8037

Epoch 00044: val_loss did not improve
Epoch 45/80
1712/1712 [==============================] - 1s 488us/step - loss: 8.2657e-04 - acc: 0.8318 - val_loss: 0.0011 - val_acc: 0.8014

Epoch 00045: val_loss did not improve
Epoch 46/80
1712/1712 [==============================] - 1s 467us/step - loss: 8.0413e-04 - acc: 0.8306 - val_loss: 0.0011 - val_acc: 0.7944

Epoch 00046: val_loss did not improve
Epoch 47/80
1712/1712 [==============================] - 1s 473us/step - loss: 8.0957e-04 - acc: 0.8300 - val_loss: 0.0012 - val_acc: 0.7991

Epoch 00047: val_loss did not improve
Epoch 48/80
1712/1712 [==============================] - 1s 479us/step - loss: 8.0088e-04 - acc: 0.8277 - val_loss: 0.0011 - val_acc: 0.7991

Epoch 00048: val_loss improved from 0.00110 to 0.00110, saving model to my_model_Nadam.h5
Epoch 49/80
1712/1712 [==============================] - 1s 473us/step - loss: 7.9929e-04 - acc: 0.8499 - val_loss: 0.0011 - val_acc: 0.7897

Epoch 00049: val_loss did not improve
Epoch 50/80
1712/1712 [==============================] - 1s 486us/step - loss: 8.0726e-04 - acc: 0.8364 - val_loss: 0.0011 - val_acc: 0.8037

Epoch 00050: val_loss improved from 0.00110 to 0.00109, saving model to my_model_Nadam.h5
Epoch 51/80
1712/1712 [==============================] - 1s 484us/step - loss: 7.8013e-04 - acc: 0.8446 - val_loss: 0.0011 - val_acc: 0.7991

Epoch 00051: val_loss did not improve
Epoch 52/80
1712/1712 [==============================] - 1s 487us/step - loss: 7.7963e-04 - acc: 0.8440 - val_loss: 0.0011 - val_acc: 0.8014

Epoch 00052: val_loss did not improve
Epoch 53/80
1712/1712 [==============================] - 1s 490us/step - loss: 7.7476e-04 - acc: 0.8347 - val_loss: 0.0011 - val_acc: 0.8014

Epoch 00053: val_loss did not improve
Epoch 54/80
1712/1712 [==============================] - 1s 471us/step - loss: 7.5096e-04 - acc: 0.8376 - val_loss: 0.0011 - val_acc: 0.7967

Epoch 00054: val_loss improved from 0.00109 to 0.00108, saving model to my_model_Nadam.h5
Epoch 55/80
1712/1712 [==============================] - 1s 482us/step - loss: 7.7481e-04 - acc: 0.8259 - val_loss: 0.0011 - val_acc: 0.8037

Epoch 00055: val_loss did not improve
Epoch 56/80
1712/1712 [==============================] - 1s 481us/step - loss: 7.7558e-04 - acc: 0.8388 - val_loss: 0.0011 - val_acc: 0.8014

Epoch 00056: val_loss did not improve
Epoch 57/80
1712/1712 [==============================] - 1s 487us/step - loss: 7.2524e-04 - acc: 0.8277 - val_loss: 0.0011 - val_acc: 0.8037

Epoch 00057: val_loss did not improve
Epoch 58/80
1712/1712 [==============================] - 1s 500us/step - loss: 7.7656e-04 - acc: 0.8429 - val_loss: 0.0011 - val_acc: 0.8037

Epoch 00058: val_loss did not improve
Epoch 59/80
1712/1712 [==============================] - 1s 480us/step - loss: 7.7039e-04 - acc: 0.8417 - val_loss: 0.0011 - val_acc: 0.7921

Epoch 00059: val_loss did not improve
Epoch 60/80
1712/1712 [==============================] - 1s 485us/step - loss: 7.4279e-04 - acc: 0.8440 - val_loss: 0.0011 - val_acc: 0.7967

Epoch 00060: val_loss did not improve
Epoch 61/80
1712/1712 [==============================] - 1s 480us/step - loss: 7.3071e-04 - acc: 0.8464 - val_loss: 0.0011 - val_acc: 0.7967

Epoch 00061: val_loss did not improve
Epoch 62/80
1712/1712 [==============================] - 1s 488us/step - loss: 7.0769e-04 - acc: 0.8440 - val_loss: 0.0011 - val_acc: 0.7991

Epoch 00062: val_loss improved from 0.00108 to 0.00107, saving model to my_model_Nadam.h5
Epoch 63/80
1712/1712 [==============================] - 1s 486us/step - loss: 7.3945e-04 - acc: 0.8364 - val_loss: 0.0010 - val_acc: 0.8061

Epoch 00063: val_loss improved from 0.00107 to 0.00104, saving model to my_model_Nadam.h5
Epoch 64/80
1712/1712 [==============================] - 1s 475us/step - loss: 6.7846e-04 - acc: 0.8446 - val_loss: 0.0011 - val_acc: 0.7827

Epoch 00064: val_loss did not improve
Epoch 65/80
1712/1712 [==============================] - 1s 477us/step - loss: 7.0454e-04 - acc: 0.8318 - val_loss: 0.0011 - val_acc: 0.7991

Epoch 00065: val_loss did not improve
Epoch 66/80
1712/1712 [==============================] - 1s 475us/step - loss: 7.2514e-04 - acc: 0.8452 - val_loss: 0.0011 - val_acc: 0.7897

Epoch 00066: val_loss did not improve
Epoch 67/80
1712/1712 [==============================] - 1s 481us/step - loss: 6.9667e-04 - acc: 0.8429 - val_loss: 0.0011 - val_acc: 0.7874

Epoch 00067: val_loss did not improve
Epoch 68/80
1712/1712 [==============================] - 1s 482us/step - loss: 6.9676e-04 - acc: 0.8551 - val_loss: 0.0011 - val_acc: 0.8084

Epoch 00068: val_loss did not improve
Epoch 69/80
1712/1712 [==============================] - 1s 463us/step - loss: 7.2338e-04 - acc: 0.8481 - val_loss: 0.0011 - val_acc: 0.7897

Epoch 00069: val_loss did not improve
Epoch 70/80
1712/1712 [==============================] - 1s 477us/step - loss: 7.2416e-04 - acc: 0.8493 - val_loss: 0.0011 - val_acc: 0.8037

Epoch 00070: val_loss did not improve
Epoch 71/80
1712/1712 [==============================] - 1s 485us/step - loss: 6.9108e-04 - acc: 0.8423 - val_loss: 0.0010 - val_acc: 0.7967

Epoch 00071: val_loss improved from 0.00104 to 0.00103, saving model to my_model_Nadam.h5
Epoch 72/80
1712/1712 [==============================] - 1s 471us/step - loss: 6.6994e-04 - acc: 0.8534 - val_loss: 0.0011 - val_acc: 0.7944

Epoch 00072: val_loss did not improve
Epoch 73/80
1712/1712 [==============================] - 1s 487us/step - loss: 6.8479e-04 - acc: 0.8546 - val_loss: 0.0011 - val_acc: 0.7897

Epoch 00073: val_loss did not improve
Epoch 74/80
1712/1712 [==============================] - 1s 485us/step - loss: 7.0012e-04 - acc: 0.8481 - val_loss: 0.0011 - val_acc: 0.7897

Epoch 00074: val_loss did not improve
Epoch 75/80
1712/1712 [==============================] - 1s 475us/step - loss: 7.0593e-04 - acc: 0.8546 - val_loss: 0.0011 - val_acc: 0.8154

Epoch 00075: val_loss did not improve
Epoch 76/80
1712/1712 [==============================] - 1s 478us/step - loss: 6.6173e-04 - acc: 0.8516 - val_loss: 0.0011 - val_acc: 0.7967

Epoch 00076: val_loss did not improve
Epoch 77/80
1712/1712 [==============================] - 1s 481us/step - loss: 7.0473e-04 - acc: 0.8522 - val_loss: 0.0011 - val_acc: 0.7944

Epoch 00077: val_loss did not improve
Epoch 78/80
1712/1712 [==============================] - 1s 475us/step - loss: 6.9478e-04 - acc: 0.8435 - val_loss: 0.0011 - val_acc: 0.7874

Epoch 00078: val_loss did not improve
Epoch 79/80
1712/1712 [==============================] - 1s 474us/step - loss: 6.6658e-04 - acc: 0.8475 - val_loss: 0.0011 - val_acc: 0.7921

Epoch 00079: val_loss did not improve
Epoch 80/80
1712/1712 [==============================] - 1s 480us/step - loss: 6.7387e-04 - acc: 0.8540 - val_loss: 0.0011 - val_acc: 0.7734

Epoch 00080: val_loss did not improve
In [83]:
# Plot to show the loss curves when using different optimizers

for name in names:
    plt.plot(histories[name].history['val_loss'])
plt.title('Validation loss with Different Optimizers')
plt.ylabel('Validation loss')
plt.xlabel('Epoch')
plt.ylim(0.0005, 0.0040)
plt.subplots_adjust(left=0.0, right=2.0, bottom=0.0, top=2.0)
plt.legend(names, loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()

Step 7: Visualize the Loss and Test Predictions

(IMPLEMENTATION) Answer a few questions and visualize the loss

Question 1: Outline the steps you took to get to your final neural network architecture and your reasoning at each step.

Answer:

I started with Adam optimizer because of the following article: http://ruder.io/optimizing-gradient-descent/index.html#adam The article mentions that adaptive optimizers are better (faster training) for deep complex neural networks, especially Adam.

I noticed that a kernel size of 2 was worse than kernel size of 3 for convolutional layer.

I started small (to speed up training) with the following model:

Conv2D 16 filters
MaxPool size 2
Conv2D 32 filters
MaxPool size 2
Flatten
Dense 64 nodes
Dense 30 output

I started with only 2 convolutioanal layers. I used 80 epochs for all runs with 20 as batch size. The architecture is based on LeNet 5 with much lower number of layers. I used enough epochs to detect when overfitting occurs.

Surprisingly I noticed that the network was overfitting as validation loss plateaued while training loss decreased. I added a dropout layer with 0.1 probability after first fully connected Dense layer with 64 nodes. I picked 0.1 since its a small number. This lead to better loss of 0.0011 with validation loss of 0.0013. I have already hit the target loss of 0.0015, as suggested by this project, at this point.

I added one more convolutional layer with 64 filters followed by a max pool layer. Based on Udacity deep learning lessons it was recommended that we increase the filters in multiples of 2 starting with 16 or 32. This new layer greatly improved the results with a loss of 0.000892 and a validation loss of 0.0011. The training and validation loss curves did not reveal signs of over fitting at this point. Since the target was met I have decided to stop at this point.

Question 2: Defend your choice of optimizer. Which optimizers did you test, and how did you determine which worked best?

Answer: I used all the optimizers in a loop and observed how the loss converges in the plot drawn above. Adam, RMPProp, Nadam and Adamax were very evenly matched since they converged to the lowest loss fastest. Adam looks like a better optimizer since it looks more stable.

Use the code cell below to plot the training and validation loss of your neural network. You may find this resource useful.

In [85]:
## TODO: Visualize the training and validation loss of your neural network

selected_optimizer = 'Adam'
hist = histories[selected_optimizer]

# summarize history for accuracy
plt.plot(hist.history['acc'])
plt.plot(hist.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()

# summarize history for loss
plt.plot(hist.history['loss'])
plt.plot(hist.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()

Question 3: Do you notice any evidence of overfitting or underfitting in the above plot? If so, what steps have you taken to improve your model? Note that slight overfitting or underfitting will not hurt your chances of a successful submission, as long as you have attempted some solutions towards improving your model (such as regularization, dropout, increased/decreased number of layers, etc).

Answer:

There was a very evident sigh of overfitting but adding the dropout layer significantly reduced this issue. I started with a very small network and gradually added more layers till I started to get first hints of overfitting.

In the latest model there is a slight overfitting trend based on the "model loss" plot shown above after about 65 epochs. The validation loss plateaued while the training loss decreased slightly.

To overcome overfitting Keras was configured to only save the model with the least validation loss, in effect using the early stopping technique.

In [158]:
model = get_model()
model.load_weights("my_model.h5")

Visualize a Subset of the Test Predictions

Execute the code cell below to visualize your model's predicted keypoints on a subset of the testing images.

In [159]:
y_test = model.predict(X_test)
fig = plt.figure(figsize=(20,20))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
for i in range(9):
    ax = fig.add_subplot(3, 3, i + 1, xticks=[], yticks=[])
    plot_data(X_test[i], y_test[i], ax)

Step 8: Complete the pipeline

With the work you did in Sections 1 and 2 of this notebook, along with your freshly trained facial keypoint detector, you can now complete the full pipeline. That is given a color image containing a person or persons you can now

  • Detect the faces in this image automatically using OpenCV
  • Predict the facial keypoints in each face detected in the image
  • Paint predicted keypoints on each face detected

In this Subsection you will do just this!

(IMPLEMENTATION) Facial Keypoints Detector

Use the OpenCV face detection functionality you built in previous Sections to expand the functionality of your keypoints detector to color images with arbitrary size. Your function should perform the following steps

  1. Accept a color image.
  2. Convert the image to grayscale.
  3. Detect and crop the face contained in the image.
  4. Locate the facial keypoints in the cropped image.
  5. Overlay the facial keypoints in the original (color, uncropped) image.

Note: step 4 can be the trickiest because remember your convolutional network is only trained to detect facial keypoints in $96 \times 96$ grayscale images where each pixel was normalized to lie in the interval $[0,1]$, and remember that each facial keypoint was normalized during training to the interval $[-1,1]$. This means - practically speaking - to paint detected keypoints onto a test face you need to perform this same pre-processing to your candidate face - that is after detecting it you should resize it to $96 \times 96$ and normalize its values before feeding it into your facial keypoint detector. To be shown correctly on the original image the output keypoints from your detector then need to be shifted and re-normalized from the interval $[-1,1]$ to the width and height of your detected face.

When complete you should be able to produce example images like the one below

In [156]:
# Load in color image for face detection
image = cv2.imread('images/obamas4.jpg')


# Convert the image to RGB colorspace
image_copy = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)


# plot our image
fig = plt.figure(figsize = (9,9))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('image copy')
ax1.imshow(image_copy)
Out[156]:
<matplotlib.image.AxesImage at 0x1fc0e8c3080>
In [160]:
### TODO: Use the face detection code we saw in Section 1 with your trained conv-net 

def get_face_boxes(gray, face_cascade):    
    faces = face_cascade.detectMultiScale(gray, 1.26, 5)
           
    return faces

def draw_face_bounding_boxes(faces, image):    
    for (x,y,w,h) in faces:    
        cv2.rectangle(image, (x, y), (x+w, y+h), (255,0,0), 2)
    
    return image


def get_CNN_Inputs(faces):
    network_inputs = []
    for (x,y,w,h) in faces:
        face_image = gray[y:y+h, x:x+w]
        resized_face = cv2.resize(face_image, (96, 96))
        resized_face_network_input = np.reshape(resized_face, (96,96,1)) / 255
        resized_face_network_input = np.array(resized_face_network_input)
        network_inputs.append(resized_face_network_input)

    network_inputs = np.array(network_inputs)

    return network_inputs

# Load in color image for face detection
image = cv2.imread('images/obamas4.jpg')

# Convert the image to RGB colorspace
image_copy = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    
gray = cv2.cvtColor(image_copy, cv2.COLOR_RGB2GRAY)

# Extract the pre-trained face detector from an xml file
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

#Get face boundaries
faces = get_face_boxes(gray, face_cascade)
image_copy = draw_face_bounding_boxes(faces, image_copy)

facial_key_points = get_facial_key_points(faces )

#Get normalized inputs to CNN
network_inputs = get_CNN_Inputs(faces)


# Setup display for plots
fig = plt.figure(figsize = (10, 10))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])

# Plot the Facial Keypoints
for i in range(predicted_points.shape[0]):
    x,y,w,h = faces[i]

    # Denormalize here
    pts_x = predicted_points[i][0::2] * w/2 + w/2 + x # odd 
    pts_y = predicted_points[i][1::2] * h/2 + h/2 + y # even

    ax1.scatter(pts_x,pts_y, marker='o', c='lawngreen', s=20) 

ax1.set_title('Image with Face Detection')
ax1.imshow(image_copy)
    
Out[160]:
<matplotlib.image.AxesImage at 0x1fc0f168438>

(Optional) Further Directions - add a filter using facial keypoints to your laptop camera

Now you can add facial keypoint detection to your laptop camera - as illustrated in the gif below.

The next Python cell contains the basic laptop video camera function used in the previous optional video exercises. Combine it with the functionality you developed for keypoint detection and marking in the previous exercise and you should be good to go!

In [ ]:
import cv2
import time 
from keras.models import load_model
def laptop_camera_go():
    # Create instance of video capturer
    cv2.namedWindow("face detection activated")
    vc = cv2.VideoCapture(0)

    # Try to get the first frame
    if vc.isOpened(): 
        rval, frame = vc.read()
    else:
        rval = False
    
    # keep video stream open
    while rval:
        # plot image from camera with detections marked
        cv2.imshow("face detection activated", frame)
        
        # exit functionality - press any key to exit laptop video
        key = cv2.waitKey(20)
        if key > 0: # exit by pressing any key
            # destroy windows
            cv2.destroyAllWindows()
            
            # hack from stack overflow for making sure window closes on osx --> https://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv
            for i in range (1,5):
                cv2.waitKey(1)
            return
        
        # read next frame
        time.sleep(0.05)             # control framerate for computation - default 20 frames per sec
        rval, frame = vc.read()  
In [ ]:
# Run your keypoint face painter
laptop_camera_go()

(Optional) Further Directions - add a filter using facial keypoints

Using your freshly minted facial keypoint detector pipeline you can now do things like add fun filters to a person's face automatically. In this optional exercise you can play around with adding sunglasses automatically to each individual's face in an image as shown in a demonstration image below.

To produce this effect an image of a pair of sunglasses shown in the Python cell below.

In [ ]:
# Load in sunglasses image - note the usage of the special option
# cv2.IMREAD_UNCHANGED, this option is used because the sunglasses 
# image has a 4th channel that allows us to control how transparent each pixel in the image is
sunglasses = cv2.imread("images/sunglasses_4.png", cv2.IMREAD_UNCHANGED)

# Plot the image
fig = plt.figure(figsize = (6,6))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.imshow(sunglasses)
ax1.axis('off');

This image is placed over each individual's face using the detected eye points to determine the location of the sunglasses, and eyebrow points to determine the size that the sunglasses should be for each person (one could also use the nose point to determine this).

Notice that this image actually has 4 channels, not just 3.

In [ ]:
# Print out the shape of the sunglasses image
print ('The sunglasses image has shape: ' + str(np.shape(sunglasses)))

It has the usual red, blue, and green channels any color image has, with the 4th channel representing the transparency level of each pixel in the image. Here's how the transparency channel works: the lower the value, the more transparent the pixel will become. The lower bound (completely transparent) is zero here, so any pixels set to 0 will not be seen.

This is how we can place this image of sunglasses on someone's face and still see the area around of their face where the sunglasses lie - because these pixels in the sunglasses image have been made completely transparent.

Lets check out the alpha channel of our sunglasses image in the next Python cell. Note because many of the pixels near the boundary are transparent we'll need to explicitly print out non-zero values if we want to see them.

In [ ]:
# Print out the sunglasses transparency (alpha) channel
alpha_channel = sunglasses[:,:,3]
print ('the alpha channel here looks like')
print (alpha_channel)

# Just to double check that there are indeed non-zero values
# Let's find and print out every value greater than zero
values = np.where(alpha_channel != 0)
print ('\n the non-zero values of the alpha channel look like')
print (values)

This means that when we place this sunglasses image on top of another image, we can use the transparency channel as a filter to tell us which pixels to overlay on a new image (only the non-transparent ones with values greater than zero).

One last thing: it's helpful to understand which keypoint belongs to the eyes, mouth, etc. So, in the image below, we also display the index of each facial keypoint directly on the image so that you can tell which keypoints are for the eyes, eyebrows, etc.

With this information, you're well on your way to completing this filtering task! See if you can place the sunglasses automatically on the individuals in the image loaded in / shown in the next Python cell.

In [ ]:
# Load in color image for face detection
image = cv2.imread('images/obamas4.jpg')

# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)


# Plot the image
fig = plt.figure(figsize = (8,8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('Original Image')
ax1.imshow(image)
In [ ]:
## (Optional) TODO: Use the face detection code we saw in Section 1 with your trained conv-net to put
## sunglasses on the individuals in our test image

(Optional) Further Directions - add a filter using facial keypoints to your laptop camera

Now you can add the sunglasses filter to your laptop camera - as illustrated in the gif below.

The next Python cell contains the basic laptop video camera function used in the previous optional video exercises. Combine it with the functionality you developed for adding sunglasses to someone's face in the previous optional exercise and you should be good to go!

In [ ]:
import cv2
import time 
from keras.models import load_model
import numpy as np

def laptop_camera_go():
    # Create instance of video capturer
    cv2.namedWindow("face detection activated")
    vc = cv2.VideoCapture(0)

    # try to get the first frame
    if vc.isOpened(): 
        rval, frame = vc.read()
    else:
        rval = False
    
    # Keep video stream open
    while rval:
        # Plot image from camera with detections marked
        cv2.imshow("face detection activated", frame)
        
        # Exit functionality - press any key to exit laptop video
        key = cv2.waitKey(20)
        if key > 0: # exit by pressing any key
            # Destroy windows 
            cv2.destroyAllWindows()
            
            for i in range (1,5):
                cv2.waitKey(1)
            return
        
        # Read next frame
        time.sleep(0.05)             # control framerate for computation - default 20 frames per sec
        rval, frame = vc.read()    
        
In [ ]:
# Load facial landmark detector model
model = load_model('my_model.h5')

# Run sunglasses painter
laptop_camera_go()